Overview

Dataset statistics

Number of variables200
Number of observations500720
Missing cells1181123
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory799.5 MiB
Average record size in memory1.6 KiB

Variable types

Text2
Boolean45
Categorical153

Alerts

is_current_customer has constant value ""Constant
is_self_service has constant value ""Constant
is_arr_over_12k has constant value ""Constant
company_revenue_bucket is highly overall correlated with revenue_Over 10B and 6 other fieldsHigh correlation
country_top is highly overall correlated with country_top_AU and 14 other fieldsHigh correlation
country_top_AU is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_BR is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_CA is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_DE is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_FR is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_GB is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_IN is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_Missing is highly overall correlated with country_top and 2 other fieldsHigh correlation
country_top_NL is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_Other is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_US is highly overall correlated with country_top and 4 other fieldsHigh correlation
crossbeam_product10_customer is highly overall correlated with crossbeam_product11_customer and 22 other fieldsHigh correlation
crossbeam_product11_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product12_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product13_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product14_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product15_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product16_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product17_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product18_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product19_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product1_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product20_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product21_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product22_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product23_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product2_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product3_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product4_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product5_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product6_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product7_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product8_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product9_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
dnb_founded_time_grouped is highly overall correlated with founded_After 2000 and 2 other fieldsHigh correlation
founded_After 2000 is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
founded_Before 2000 is highly overall correlated with dnb_founded_time_groupedHigh correlation
founded_nan is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
has_crossbeam_data is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
has_hg_data is highly overall correlated with hg_product_100 and 123 other fieldsHigh correlation
hg_product_100 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_101 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_102 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_103 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_104 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_105 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_106 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_107 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_108 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_109 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_110 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_111 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_112 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_113 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_114 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_115 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_116 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_117 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_118 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_119 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_120 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_121 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_122 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_123 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_124 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_125 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_126 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_127 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_128 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_129 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_130 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_131 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_132 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_133 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_134 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_135 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_136 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_137 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_138 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_139 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_140 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_141 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_142 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_143 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_144 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_145 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_146 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_147 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_148 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_27 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_28 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_29 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_30 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_31 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_32 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_33 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_34 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_35 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_36 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_37 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_38 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_39 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_40 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_41 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_42 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_43 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_44 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_45 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_46 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_47 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_48 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_49 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_50 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_51 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_52 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_53 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_54 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_55 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_56 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_57 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_58 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_59 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_60 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_61 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_62 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_63 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_64 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_65 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_66 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_67 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_68 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_69 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_70 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_71 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_72 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_73 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_74 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_75 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_76 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_77 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_78 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_79 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_80 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_81 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_82 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_83 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_84 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_85 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_86 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_87 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_88 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_89 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_90 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_91 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_92 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_93 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_94 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_95 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_96 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_97 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_98 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_99 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
industry_1. Tech - Computer systems design and related services is highly overall correlated with industry_groupedHigh correlation
industry_2. Tech - Software Publisher is highly overall correlated with industry_groupedHigh correlation
industry_3. Finance is highly overall correlated with industry_groupedHigh correlation
industry_4. Consulting is highly overall correlated with industry_groupedHigh correlation
industry_5. Retail is highly overall correlated with industry_groupedHigh correlation
industry_6. Manufacturing is highly overall correlated with industry_groupedHigh correlation
industry_7. Wholesale Trade is highly overall correlated with industry_groupedHigh correlation
industry_8. Healthcare is highly overall correlated with industry_groupedHigh correlation
industry_9. Other is highly overall correlated with industry_groupedHigh correlation
industry_grouped is highly overall correlated with industry_1. Tech - Computer systems design and related services and 9 other fieldsHigh correlation
industry_nan is highly overall correlated with country_top and 2 other fieldsHigh correlation
revenue_Over 10B is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 100M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 10B is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 10M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 1B is highly overall correlated with company_revenue_bucket and 124 other fieldsHigh correlation
revenue_Under 1M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_nan is highly overall correlated with company_revenue_bucket and 124 other fieldsHigh correlation
state is highly overall correlated with country_top and 23 other fieldsHigh correlation
state_top is highly overall correlated with country_top_US and 12 other fieldsHigh correlation
state_top_CA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_CO is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_FL is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_GA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_IL is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_MA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_Missing is highly overall correlated with country_top and 4 other fieldsHigh correlation
state_top_NY is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_Other is highly overall correlated with country_top and 4 other fieldsHigh correlation
state_top_TX is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_WA is highly overall correlated with state and 1 other fieldsHigh correlation
has_hg_data is highly imbalanced (55.5%)Imbalance
hg_product_27 is highly imbalanced (71.4%)Imbalance
hg_product_28 is highly imbalanced (69.3%)Imbalance
hg_product_29 is highly imbalanced (70.7%)Imbalance
hg_product_30 is highly imbalanced (70.8%)Imbalance
hg_product_31 is highly imbalanced (68.1%)Imbalance
hg_product_32 is highly imbalanced (65.8%)Imbalance
hg_product_33 is highly imbalanced (67.1%)Imbalance
hg_product_34 is highly imbalanced (67.0%)Imbalance
hg_product_35 is highly imbalanced (71.1%)Imbalance
hg_product_36 is highly imbalanced (68.9%)Imbalance
hg_product_37 is highly imbalanced (69.6%)Imbalance
hg_product_38 is highly imbalanced (68.2%)Imbalance
hg_product_39 is highly imbalanced (68.6%)Imbalance
hg_product_40 is highly imbalanced (71.2%)Imbalance
hg_product_41 is highly imbalanced (69.1%)Imbalance
hg_product_42 is highly imbalanced (69.1%)Imbalance
hg_product_43 is highly imbalanced (70.8%)Imbalance
hg_product_44 is highly imbalanced (68.8%)Imbalance
hg_product_45 is highly imbalanced (68.3%)Imbalance
hg_product_46 is highly imbalanced (71.3%)Imbalance
hg_product_47 is highly imbalanced (66.1%)Imbalance
hg_product_48 is highly imbalanced (65.6%)Imbalance
hg_product_49 is highly imbalanced (70.2%)Imbalance
hg_product_50 is highly imbalanced (68.2%)Imbalance
hg_product_51 is highly imbalanced (69.9%)Imbalance
hg_product_52 is highly imbalanced (70.3%)Imbalance
hg_product_53 is highly imbalanced (70.7%)Imbalance
hg_product_54 is highly imbalanced (71.4%)Imbalance
hg_product_55 is highly imbalanced (67.2%)Imbalance
hg_product_56 is highly imbalanced (71.3%)Imbalance
hg_product_57 is highly imbalanced (69.3%)Imbalance
hg_product_58 is highly imbalanced (71.4%)Imbalance
hg_product_59 is highly imbalanced (71.4%)Imbalance
hg_product_60 is highly imbalanced (71.2%)Imbalance
hg_product_61 is highly imbalanced (71.4%)Imbalance
hg_product_62 is highly imbalanced (69.5%)Imbalance
hg_product_63 is highly imbalanced (68.0%)Imbalance
hg_product_64 is highly imbalanced (71.3%)Imbalance
hg_product_65 is highly imbalanced (69.6%)Imbalance
hg_product_66 is highly imbalanced (70.9%)Imbalance
hg_product_67 is highly imbalanced (65.6%)Imbalance
hg_product_68 is highly imbalanced (68.9%)Imbalance
hg_product_69 is highly imbalanced (68.5%)Imbalance
hg_product_70 is highly imbalanced (70.4%)Imbalance
hg_product_71 is highly imbalanced (70.9%)Imbalance
hg_product_72 is highly imbalanced (69.5%)Imbalance
hg_product_73 is highly imbalanced (65.4%)Imbalance
hg_product_74 is highly imbalanced (69.8%)Imbalance
hg_product_75 is highly imbalanced (69.3%)Imbalance
hg_product_76 is highly imbalanced (68.5%)Imbalance
hg_product_77 is highly imbalanced (68.4%)Imbalance
hg_product_78 is highly imbalanced (70.1%)Imbalance
hg_product_79 is highly imbalanced (69.2%)Imbalance
hg_product_80 is highly imbalanced (70.2%)Imbalance
hg_product_81 is highly imbalanced (69.5%)Imbalance
hg_product_82 is highly imbalanced (71.4%)Imbalance
hg_product_83 is highly imbalanced (66.6%)Imbalance
hg_product_84 is highly imbalanced (70.8%)Imbalance
hg_product_85 is highly imbalanced (71.4%)Imbalance
hg_product_86 is highly imbalanced (70.8%)Imbalance
hg_product_87 is highly imbalanced (65.7%)Imbalance
hg_product_88 is highly imbalanced (68.9%)Imbalance
hg_product_89 is highly imbalanced (70.6%)Imbalance
hg_product_90 is highly imbalanced (70.4%)Imbalance
hg_product_91 is highly imbalanced (71.4%)Imbalance
hg_product_92 is highly imbalanced (71.0%)Imbalance
hg_product_93 is highly imbalanced (71.4%)Imbalance
hg_product_94 is highly imbalanced (71.3%)Imbalance
hg_product_95 is highly imbalanced (70.0%)Imbalance
hg_product_96 is highly imbalanced (69.2%)Imbalance
hg_product_97 is highly imbalanced (71.1%)Imbalance
hg_product_98 is highly imbalanced (70.9%)Imbalance
hg_product_99 is highly imbalanced (71.0%)Imbalance
hg_product_100 is highly imbalanced (70.4%)Imbalance
hg_product_101 is highly imbalanced (70.6%)Imbalance
hg_product_102 is highly imbalanced (66.6%)Imbalance
hg_product_103 is highly imbalanced (68.9%)Imbalance
hg_product_104 is highly imbalanced (70.9%)Imbalance
hg_product_105 is highly imbalanced (69.5%)Imbalance
hg_product_106 is highly imbalanced (71.2%)Imbalance
hg_product_107 is highly imbalanced (71.2%)Imbalance
hg_product_108 is highly imbalanced (71.4%)Imbalance
hg_product_109 is highly imbalanced (71.4%)Imbalance
hg_product_110 is highly imbalanced (71.3%)Imbalance
hg_product_111 is highly imbalanced (71.4%)Imbalance
hg_product_112 is highly imbalanced (69.9%)Imbalance
hg_product_113 is highly imbalanced (71.4%)Imbalance
hg_product_114 is highly imbalanced (70.0%)Imbalance
hg_product_115 is highly imbalanced (71.3%)Imbalance
hg_product_116 is highly imbalanced (71.0%)Imbalance
hg_product_117 is highly imbalanced (71.3%)Imbalance
hg_product_118 is highly imbalanced (71.4%)Imbalance
hg_product_119 is highly imbalanced (69.5%)Imbalance
hg_product_120 is highly imbalanced (71.0%)Imbalance
hg_product_121 is highly imbalanced (71.3%)Imbalance
hg_product_122 is highly imbalanced (71.4%)Imbalance
hg_product_123 is highly imbalanced (70.6%)Imbalance
hg_product_124 is highly imbalanced (66.8%)Imbalance
hg_product_125 is highly imbalanced (66.2%)Imbalance
hg_product_126 is highly imbalanced (70.8%)Imbalance
hg_product_127 is highly imbalanced (70.9%)Imbalance
hg_product_128 is highly imbalanced (71.4%)Imbalance
hg_product_129 is highly imbalanced (65.6%)Imbalance
hg_product_130 is highly imbalanced (71.3%)Imbalance
hg_product_131 is highly imbalanced (71.2%)Imbalance
hg_product_132 is highly imbalanced (68.5%)Imbalance
hg_product_133 is highly imbalanced (71.2%)Imbalance
hg_product_134 is highly imbalanced (71.3%)Imbalance
hg_product_135 is highly imbalanced (71.4%)Imbalance
hg_product_136 is highly imbalanced (71.4%)Imbalance
hg_product_137 is highly imbalanced (71.0%)Imbalance
hg_product_138 is highly imbalanced (71.2%)Imbalance
hg_product_139 is highly imbalanced (69.2%)Imbalance
hg_product_140 is highly imbalanced (67.9%)Imbalance
hg_product_141 is highly imbalanced (70.6%)Imbalance
hg_product_142 is highly imbalanced (71.0%)Imbalance
hg_product_143 is highly imbalanced (68.9%)Imbalance
hg_product_144 is highly imbalanced (66.2%)Imbalance
hg_product_145 is highly imbalanced (68.1%)Imbalance
hg_product_146 is highly imbalanced (71.2%)Imbalance
hg_product_147 is highly imbalanced (71.1%)Imbalance
hg_product_148 is highly imbalanced (71.4%)Imbalance
country_top_AU is highly imbalanced (80.5%)Imbalance
country_top_BR is highly imbalanced (88.9%)Imbalance
country_top_CA is highly imbalanced (79.5%)Imbalance
country_top_DE is highly imbalanced (75.8%)Imbalance
country_top_FR is highly imbalanced (79.7%)Imbalance
country_top_GB is highly imbalanced (57.3%)Imbalance
country_top_IN is highly imbalanced (65.2%)Imbalance
country_top_Missing is highly imbalanced (60.1%)Imbalance
country_top_NL is highly imbalanced (86.9%)Imbalance
state_top_CA is highly imbalanced (59.8%)Imbalance
state_top_CO is highly imbalanced (90.9%)Imbalance
state_top_FL is highly imbalanced (85.7%)Imbalance
state_top_GA is highly imbalanced (91.6%)Imbalance
state_top_IL is highly imbalanced (88.5%)Imbalance
state_top_MA is highly imbalanced (89.3%)Imbalance
state_top_NY is highly imbalanced (75.8%)Imbalance
state_top_TX is highly imbalanced (82.6%)Imbalance
state_top_WA is highly imbalanced (90.6%)Imbalance
industry_2. Tech - Software Publisher is highly imbalanced (90.3%)Imbalance
industry_3. Finance is highly imbalanced (69.5%)Imbalance
industry_4. Consulting is highly imbalanced (76.8%)Imbalance
industry_5. Retail is highly imbalanced (72.9%)Imbalance
industry_6. Manufacturing is highly imbalanced (60.9%)Imbalance
industry_7. Wholesale Trade is highly imbalanced (75.8%)Imbalance
industry_8. Healthcare is highly imbalanced (80.7%)Imbalance
revenue_Over 10B is highly imbalanced (95.6%)Imbalance
revenue_Under 100M is highly imbalanced (86.8%)Imbalance
revenue_Under 10B is highly imbalanced (88.2%)Imbalance
revenue_Under 10M is highly imbalanced (96.8%)Imbalance
revenue_Under 1B is highly imbalanced (77.4%)Imbalance
revenue_Under 1M is highly imbalanced (98.4%)Imbalance
revenue_nan is highly imbalanced (59.7%)Imbalance
company_revenue_bucket has 460489 (92.0%) missing valuesMissing
country has 39646 (7.9%) missing valuesMissing
state has 315976 (63.1%) missing valuesMissing
industry_grouped has 68795 (13.7%) missing valuesMissing
dnb_founded_time_grouped has 296217 (59.2%) missing valuesMissing
account_id has unique valuesUnique

Reproduction

Analysis started2023-12-31 21:31:19.254978
Analysis finished2023-12-31 21:48:03.498511
Duration16 minutes and 44.24 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

account_id
Text

UNIQUE 

Distinct500720
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.6 MiB
2023-12-31T13:48:04.035758image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters9012960
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500720 ?
Unique (%)100.0%

Sample

1st row001PY000002SuJDYA0
2nd row0011G00000nEkzYQAS
3rd row0011G00000qFM5EQAW
4th row0011G00000tKtSbQAK
5th row0011G00000tLivvQAC
ValueCountFrequency (%)
001py000002sujdya0 1
 
< 0.1%
0011g00000qfm5eqaw 1
 
< 0.1%
0011g00000tlivvqac 1
 
< 0.1%
0011g00000uogzkqa2 1
 
< 0.1%
0011g00000ufx52qaa 1
 
< 0.1%
0011g00000xa4ekqa0 1
 
< 0.1%
0011g0000120dn1qai 1
 
< 0.1%
0011g0000135y3dqaa 1
 
< 0.1%
001ho000013liqniau 1
 
< 0.1%
001ho000015sspjiac 1
 
< 0.1%
Other values (500710) 500710
> 99.9%
2023-12-31T13:48:04.908028image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3470983
38.5%
1 1056139
 
11.7%
A 600070
 
6.7%
Q 482871
 
5.4%
G 464404
 
5.2%
Y 136070
 
1.5%
I 128365
 
1.4%
w 101166
 
1.1%
o 99061
 
1.1%
n 86547
 
1.0%
Other values (52) 2387284
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4912399
54.5%
Uppercase Letter 2796252
31.0%
Lowercase Letter 1304309
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 600070
21.5%
Q 482871
17.3%
G 464404
16.6%
Y 136070
 
4.9%
I 128365
 
4.6%
H 84961
 
3.0%
U 84901
 
3.0%
C 76729
 
2.7%
E 75419
 
2.7%
K 67404
 
2.4%
Other values (16) 595058
21.3%
Lowercase Letter
ValueCountFrequency (%)
w 101166
 
7.8%
o 99061
 
7.6%
n 86547
 
6.6%
h 75708
 
5.8%
k 74375
 
5.7%
q 64857
 
5.0%
j 60147
 
4.6%
u 60018
 
4.6%
g 55297
 
4.2%
y 47568
 
3.6%
Other values (16) 579565
44.4%
Decimal Number
ValueCountFrequency (%)
0 3470983
70.7%
1 1056139
 
21.5%
2 77353
 
1.6%
3 58719
 
1.2%
7 48470
 
1.0%
4 43246
 
0.9%
5 42965
 
0.9%
9 40602
 
0.8%
6 38070
 
0.8%
8 35852
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4912399
54.5%
Latin 4100561
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 600070
 
14.6%
Q 482871
 
11.8%
G 464404
 
11.3%
Y 136070
 
3.3%
I 128365
 
3.1%
w 101166
 
2.5%
o 99061
 
2.4%
n 86547
 
2.1%
H 84961
 
2.1%
U 84901
 
2.1%
Other values (42) 1832145
44.7%
Common
ValueCountFrequency (%)
0 3470983
70.7%
1 1056139
 
21.5%
2 77353
 
1.6%
3 58719
 
1.2%
7 48470
 
1.0%
4 43246
 
0.9%
5 42965
 
0.9%
9 40602
 
0.8%
6 38070
 
0.8%
8 35852
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9012960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3470983
38.5%
1 1056139
 
11.7%
A 600070
 
6.7%
Q 482871
 
5.4%
G 464404
 
5.2%
Y 136070
 
1.5%
I 128365
 
1.4%
w 101166
 
1.1%
o 99061
 
1.1%
n 86547
 
1.0%
Other values (52) 2387284
26.5%

is_current_customer
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
500720 
ValueCountFrequency (%)
False 500720
100.0%
2023-12-31T13:48:05.120247image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

is_self_service
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
500720 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1502160
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500720
100.0%

Length

2023-12-31T13:48:05.282525image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:05.432318image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500720
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1001440
66.7%
. 500720
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
66.7%
Other Punctuation 500720
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1001440
100.0%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1502160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1001440
66.7%
. 500720
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1502160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1001440
66.7%
. 500720
33.3%

is_arr_over_12k
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
500720 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1502160
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500720
100.0%

Length

2023-12-31T13:48:05.588282image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:05.753097image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500720
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1001440
66.7%
. 500720
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
66.7%
Other Punctuation 500720
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1001440
100.0%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1502160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1001440
66.7%
. 500720
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1502160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1001440
66.7%
. 500720
33.3%

company_revenue_bucket
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing460489
Missing (%)92.0%
Memory size34.4 MiB
Under 1B
18319 
Under 100M
9139 
Under 10B
7949 
Over 10B
2406 
Under 10M
 
1656

Length

Max length10
Median length8
Mean length8.6930725
Min length8

Characters and Unicode

Total characters349731
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnder 10B
2nd rowOver 10B
3rd rowUnder 1B
4th rowOver 10B
5th rowUnder 10B

Common Values

ValueCountFrequency (%)
Under 1B 18319
 
3.7%
Under 100M 9139
 
1.8%
Under 10B 7949
 
1.6%
Over 10B 2406
 
0.5%
Under 10M 1656
 
0.3%
Under 1M 762
 
0.2%
(Missing) 460489
92.0%

Length

2023-12-31T13:48:05.929567image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:06.127332image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
under 37825
47.0%
1b 18319
22.8%
10b 10355
 
12.9%
100m 9139
 
11.4%
over 2406
 
3.0%
10m 1656
 
2.1%
1m 762
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 40231
11.5%
r 40231
11.5%
40231
11.5%
1 40231
11.5%
U 37825
10.8%
n 37825
10.8%
d 37825
10.8%
0 30289
8.7%
B 28674
8.2%
M 11557
 
3.3%
Other values (2) 4812
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 158518
45.3%
Uppercase Letter 80462
23.0%
Decimal Number 70520
20.2%
Space Separator 40231
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 40231
25.4%
r 40231
25.4%
n 37825
23.9%
d 37825
23.9%
v 2406
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
U 37825
47.0%
B 28674
35.6%
M 11557
 
14.4%
O 2406
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 40231
57.0%
0 30289
43.0%
Space Separator
ValueCountFrequency (%)
40231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 238980
68.3%
Common 110751
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 40231
16.8%
r 40231
16.8%
U 37825
15.8%
n 37825
15.8%
d 37825
15.8%
B 28674
12.0%
M 11557
 
4.8%
O 2406
 
1.0%
v 2406
 
1.0%
Common
ValueCountFrequency (%)
40231
36.3%
1 40231
36.3%
0 30289
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 40231
11.5%
r 40231
11.5%
40231
11.5%
1 40231
11.5%
U 37825
10.8%
n 37825
10.8%
d 37825
10.8%
0 30289
8.7%
B 28674
8.2%
M 11557
 
3.3%
Other values (2) 4812
 
1.4%

country
Text

MISSING 

Distinct254
Distinct (%)0.1%
Missing39646
Missing (%)7.9%
Memory size31.0 MiB
2023-12-31T13:48:06.573083image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length14
Median length2
Mean length2.000334
Min length2

Characters and Unicode

Total characters922302
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowCH
2nd rowNL
3rd rowRO
4th rowIN
5th rowUS
ValueCountFrequency (%)
us 187692
40.7%
gb 43666
 
9.5%
in 32637
 
7.1%
de 19998
 
4.3%
ca 16043
 
3.5%
fr 15916
 
3.5%
au 15052
 
3.3%
nl 9102
 
2.0%
br 7408
 
1.6%
jp 6597
 
1.4%
Other values (239) 106966
23.2%
2023-12-31T13:48:07.261578image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 207866
22.5%
S 206503
22.4%
B 56038
 
6.1%
N 53741
 
5.8%
G 51638
 
5.6%
I 50243
 
5.4%
E 44546
 
4.8%
A 43215
 
4.7%
R 31841
 
3.5%
C 29855
 
3.2%
Other values (45) 146816
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 922113
> 99.9%
Lowercase Letter 175
 
< 0.1%
Decimal Number 8
 
< 0.1%
Space Separator 3
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 207866
22.5%
S 206503
22.4%
B 56038
 
6.1%
N 53741
 
5.8%
G 51638
 
5.6%
I 50243
 
5.4%
E 44546
 
4.8%
A 43215
 
4.7%
R 31841
 
3.5%
C 29855
 
3.2%
Other values (16) 146627
15.9%
Lowercase Letter
ValueCountFrequency (%)
e 24
13.7%
a 19
10.9%
n 17
9.7%
t 15
8.6%
l 14
 
8.0%
r 14
 
8.0%
d 11
 
6.3%
i 11
 
6.3%
s 7
 
4.0%
u 7
 
4.0%
Other values (11) 36
20.6%
Decimal Number
ValueCountFrequency (%)
2 2
25.0%
7 2
25.0%
1 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
√ 2
100.0%
Other Symbol
ValueCountFrequency (%)
© 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 922288
> 99.9%
Common 14
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 207866
22.5%
S 206503
22.4%
B 56038
 
6.1%
N 53741
 
5.8%
G 51638
 
5.6%
I 50243
 
5.4%
E 44546
 
4.8%
A 43215
 
4.7%
R 31841
 
3.5%
C 29855
 
3.2%
Other values (37) 146802
15.9%
Common
ValueCountFrequency (%)
3
21.4%
√ 2
14.3%
2 2
14.3%
7 2
14.3%
1 2
14.3%
© 1
 
7.1%
0 1
 
7.1%
8 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 922298
> 99.9%
Math Operators 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 207866
22.5%
S 206503
22.4%
B 56038
 
6.1%
N 53741
 
5.8%
G 51638
 
5.6%
I 50243
 
5.4%
E 44546
 
4.8%
A 43215
 
4.7%
R 31841
 
3.5%
C 29855
 
3.2%
Other values (42) 146812
15.9%
Math Operators
ValueCountFrequency (%)
√ 2
100.0%
None
ValueCountFrequency (%)
© 1
50.0%
õ 1
50.0%

state
Categorical

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)< 0.1%
Missing315976
Missing (%)63.1%
Memory size33.5 MiB
CA
40081 
NY
20048 
TX
13005 
FL
10143 
IL
 
7708
Other values (45)
93759 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters369488
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMN
2nd rowMD
3rd rowFL
4th rowNH
5th rowWA

Common Values

ValueCountFrequency (%)
CA 40081
 
8.0%
NY 20048
 
4.0%
TX 13005
 
2.6%
FL 10143
 
2.0%
IL 7708
 
1.5%
MA 7049
 
1.4%
WA 5989
 
1.2%
CO 5776
 
1.2%
GA 5223
 
1.0%
PA 5085
 
1.0%
Other values (40) 64637
 
12.9%
(Missing) 315976
63.1%

Length

2023-12-31T13:48:07.515273image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 40081
21.7%
ny 20048
 
10.9%
tx 13005
 
7.0%
fl 10143
 
5.5%
il 7708
 
4.2%
ma 7049
 
3.8%
wa 5989
 
3.2%
co 5776
 
3.1%
ga 5223
 
2.8%
pa 5085
 
2.8%
Other values (40) 64637
35.0%

Most occurring characters

ValueCountFrequency (%)
A 75154
20.3%
C 53401
14.5%
N 40714
11.0%
Y 21561
 
5.8%
T 20783
 
5.6%
M 20342
 
5.5%
L 19883
 
5.4%
I 18245
 
4.9%
O 16274
 
4.4%
X 13005
 
3.5%
Other values (14) 70126
19.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 369488
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 75154
20.3%
C 53401
14.5%
N 40714
11.0%
Y 21561
 
5.8%
T 20783
 
5.6%
M 20342
 
5.5%
L 19883
 
5.4%
I 18245
 
4.9%
O 16274
 
4.4%
X 13005
 
3.5%
Other values (14) 70126
19.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 369488
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 75154
20.3%
C 53401
14.5%
N 40714
11.0%
Y 21561
 
5.8%
T 20783
 
5.6%
M 20342
 
5.5%
L 19883
 
5.4%
I 18245
 
4.9%
O 16274
 
4.4%
X 13005
 
3.5%
Other values (14) 70126
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 369488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 75154
20.3%
C 53401
14.5%
N 40714
11.0%
Y 21561
 
5.8%
T 20783
 
5.6%
M 20342
 
5.5%
L 19883
 
5.4%
I 18245
 
4.9%
O 16274
 
4.4%
X 13005
 
3.5%
Other values (14) 70126
19.0%

industry_grouped
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing68795
Missing (%)13.7%
Memory size38.2 MiB
9. Other
224281 
1. Tech - Computer systems design and related services
58578 
6. Manufacturing
38479 
3. Finance
27305 
5. Retail
23214 
Other values (4)
60068 

Length

Max length54
Median length8
Mean length16.276168
Min length8

Characters and Unicode

Total characters7030084
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9. Other
2nd row9. Other
3rd row9. Other
4th row3. Finance
5th row9. Other

Common Values

ValueCountFrequency (%)
9. Other 224281
44.8%
1. Tech - Computer systems design and related services 58578
 
11.7%
6. Manufacturing 38479
 
7.7%
3. Finance 27305
 
5.5%
5. Retail 23214
 
4.6%
7. Wholesale Trade 20001
 
4.0%
4. Consulting 18949
 
3.8%
8. Healthcare 14845
 
3.0%
2. Tech - Software Publisher 6273
 
1.3%
(Missing) 68795
 
13.7%

Length

2023-12-31T13:48:07.706002image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:07.905527image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
9 224281
17.1%
other 224281
17.1%
tech 64851
 
4.9%
64851
 
4.9%
1 58578
 
4.5%
computer 58578
 
4.5%
systems 58578
 
4.5%
design 58578
 
4.5%
and 58578
 
4.5%
related 58578
 
4.5%
Other values (17) 382984
29.2%

Most occurring characters

ValueCountFrequency (%)
880791
 
12.5%
e 851936
 
12.1%
t 501775
 
7.1%
r 485886
 
6.9%
. 431925
 
6.1%
s 396691
 
5.6%
h 330251
 
4.7%
a 320598
 
4.6%
n 286622
 
4.1%
i 231376
 
3.3%
Other values (33) 2312233
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4697542
66.8%
Space Separator 880791
 
12.5%
Uppercase Letter 523050
 
7.4%
Other Punctuation 431925
 
6.1%
Decimal Number 431925
 
6.1%
Dash Punctuation 64851
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 851936
18.1%
t 501775
10.7%
r 485886
10.3%
s 396691
8.4%
h 330251
 
7.0%
a 320598
 
6.8%
n 286622
 
6.1%
i 231376
 
4.9%
c 204058
 
4.3%
d 195735
 
4.2%
Other values (11) 892614
19.0%
Uppercase Letter
ValueCountFrequency (%)
O 224281
42.9%
T 84852
 
16.2%
C 77527
 
14.8%
M 38479
 
7.4%
F 27305
 
5.2%
R 23214
 
4.4%
W 20001
 
3.8%
H 14845
 
2.8%
S 6273
 
1.2%
P 6273
 
1.2%
Decimal Number
ValueCountFrequency (%)
9 224281
51.9%
1 58578
 
13.6%
6 38479
 
8.9%
3 27305
 
6.3%
5 23214
 
5.4%
7 20001
 
4.6%
4 18949
 
4.4%
8 14845
 
3.4%
2 6273
 
1.5%
Space Separator
ValueCountFrequency (%)
880791
100.0%
Other Punctuation
ValueCountFrequency (%)
. 431925
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64851
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5220592
74.3%
Common 1809492
 
25.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 851936
16.3%
t 501775
 
9.6%
r 485886
 
9.3%
s 396691
 
7.6%
h 330251
 
6.3%
a 320598
 
6.1%
n 286622
 
5.5%
i 231376
 
4.4%
O 224281
 
4.3%
c 204058
 
3.9%
Other values (21) 1387118
26.6%
Common
ValueCountFrequency (%)
880791
48.7%
. 431925
23.9%
9 224281
 
12.4%
- 64851
 
3.6%
1 58578
 
3.2%
6 38479
 
2.1%
3 27305
 
1.5%
5 23214
 
1.3%
7 20001
 
1.1%
4 18949
 
1.0%
Other values (2) 21118
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7030084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
880791
 
12.5%
e 851936
 
12.1%
t 501775
 
7.1%
r 485886
 
6.9%
. 431925
 
6.1%
s 396691
 
5.6%
h 330251
 
4.7%
a 320598
 
4.6%
n 286622
 
4.1%
i 231376
 
3.3%
Other values (33) 2312233
32.9%

has_crossbeam_data
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
325968 
True
174752 
ValueCountFrequency (%)
False 325968
65.1%
True 174752
34.9%
2023-12-31T13:48:08.179152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

crossbeam_product1_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175027 
1.0
 
176

Length

Max length4
Median length3
Mean length3.3495506
Min length3

Characters and Unicode

Total characters1677187
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175027
35.0%
1.0 176
 
< 0.1%

Length

2023-12-31T13:48:08.352132image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:08.510842image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175027
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175027
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175027
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175027
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175027
 
10.4%

crossbeam_product2_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175080 
1.0
 
123

Length

Max length4
Median length3
Mean length3.3496565
Min length3

Characters and Unicode

Total characters1677240
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175080
35.0%
1.0 123
 
< 0.1%

Length

2023-12-31T13:48:08.707763image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:08.867751image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175080
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175080
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175080
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175080
 
10.4%

crossbeam_product3_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175074 
1.0
 
129

Length

Max length4
Median length3
Mean length3.3496445
Min length3

Characters and Unicode

Total characters1677234
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175074
35.0%
1.0 129
 
< 0.1%

Length

2023-12-31T13:48:09.044415image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:09.203551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175074
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175074
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175074
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175074
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175074
 
10.4%

crossbeam_product4_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175160 
1.0
 
43

Length

Max length4
Median length3
Mean length3.3498163
Min length3

Characters and Unicode

Total characters1677320
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175160
35.0%
1.0 43
 
< 0.1%

Length

2023-12-31T13:48:09.376818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:09.536650image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175160
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175160
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175160
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175160
 
10.4%

crossbeam_product5_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174466 
1.0
 
737

Length

Max length4
Median length3
Mean length3.3484303
Min length3

Characters and Unicode

Total characters1676626
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174466
34.8%
1.0 737
 
0.1%

Length

2023-12-31T13:48:09.711389image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:09.873800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174466
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174466
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676626
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174466
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174466
 
10.4%

crossbeam_product6_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
168280 
1.0
 
6923

Length

Max length4
Median length3
Mean length3.3360761
Min length3

Characters and Unicode

Total characters1670440
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 168280
33.6%
1.0 6923
 
1.4%

Length

2023-12-31T13:48:10.062622image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:10.222555image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.5%
. 500720
30.0%
1 175203
 
10.5%
- 168280
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
60.0%
Other Punctuation 500720
30.0%
Dash Punctuation 168280
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 168280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1670440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.5%
. 500720
30.0%
1 175203
 
10.5%
- 168280
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1670440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.5%
. 500720
30.0%
1 175203
 
10.5%
- 168280
 
10.1%

crossbeam_product7_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174584 
1.0
 
619

Length

Max length4
Median length3
Mean length3.3486659
Min length3

Characters and Unicode

Total characters1676744
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174584
34.9%
1.0 619
 
0.1%

Length

2023-12-31T13:48:10.402236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:10.562991image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174584
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174584
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174584
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676744
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174584
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174584
 
10.4%

crossbeam_product8_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174648 
1.0
 
555

Length

Max length4
Median length3
Mean length3.3487937
Min length3

Characters and Unicode

Total characters1676808
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174648
34.9%
1.0 555
 
0.1%

Length

2023-12-31T13:48:10.746786image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:10.916640image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174648
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174648
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174648
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174648
 
10.4%

crossbeam_product9_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175015 
1.0
 
188

Length

Max length4
Median length3
Mean length3.3495267
Min length3

Characters and Unicode

Total characters1677175
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175015
35.0%
1.0 188
 
< 0.1%

Length

2023-12-31T13:48:11.098043image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:11.586726image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175015
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175015
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175015
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175015
 
10.4%

crossbeam_product10_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
173985 
1.0
 
1218

Length

Max length4
Median length3
Mean length3.3474696
Min length3

Characters and Unicode

Total characters1676145
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 173985
34.7%
1.0 1218
 
0.2%

Length

2023-12-31T13:48:11.763818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:11.925092image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 173985
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 173985
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 173985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676145
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 173985
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 173985
 
10.4%

crossbeam_product11_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174926 
1.0
 
277

Length

Max length4
Median length3
Mean length3.3493489
Min length3

Characters and Unicode

Total characters1677086
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174926
34.9%
1.0 277
 
0.1%

Length

2023-12-31T13:48:12.097563image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:12.258565image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174926
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174926
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677086
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174926
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174926
 
10.4%

crossbeam_product12_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174344 
1.0
 
859

Length

Max length4
Median length3
Mean length3.3481866
Min length3

Characters and Unicode

Total characters1676504
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174344
34.8%
1.0 859
 
0.2%

Length

2023-12-31T13:48:12.432567image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:12.590896image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 174344
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174344
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 174344
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 174344
 
10.4%

crossbeam_product13_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175105 
1.0
 
98

Length

Max length4
Median length3
Mean length3.3497064
Min length3

Characters and Unicode

Total characters1677265
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175105
35.0%
1.0 98
 
< 0.1%

Length

2023-12-31T13:48:12.773812image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:12.934237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175105
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175105
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175105
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175105
 
10.4%

crossbeam_product14_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174015 
1.0
 
1188

Length

Max length4
Median length3
Mean length3.3475296
Min length3

Characters and Unicode

Total characters1676175
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174015
34.8%
1.0 1188
 
0.2%

Length

2023-12-31T13:48:13.122875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:13.282988image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 174015
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174015
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 174015
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 174015
 
10.4%

crossbeam_product15_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
170715 
1.0
 
4488

Length

Max length4
Median length3
Mean length3.340939
Min length3

Characters and Unicode

Total characters1672875
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 170715
34.1%
1.0 4488
 
0.9%

Length

2023-12-31T13:48:13.459652image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:13.618344image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.4%
. 500720
29.9%
1 175203
 
10.5%
- 170715
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.9%
Other Punctuation 500720
29.9%
Dash Punctuation 170715
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1672875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.4%
. 500720
29.9%
1 175203
 
10.5%
- 170715
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1672875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.4%
. 500720
29.9%
1 175203
 
10.5%
- 170715
 
10.2%

crossbeam_product16_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175045 
1.0
 
158

Length

Max length4
Median length3
Mean length3.3495866
Min length3

Characters and Unicode

Total characters1677205
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175045
35.0%
1.0 158
 
< 0.1%

Length

2023-12-31T13:48:13.794684image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:13.958502image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175045
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175045
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175045
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175045
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175045
 
10.4%

crossbeam_product17_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
175012 
1.0
 
191

Length

Max length4
Median length3
Mean length3.3495207
Min length3

Characters and Unicode

Total characters1677172
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 175012
35.0%
1.0 191
 
< 0.1%

Length

2023-12-31T13:48:14.133208image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:14.294332image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175012
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 175012
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1677172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175012
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1677172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 175012
 
10.4%

crossbeam_product18_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174754 
1.0
 
449

Length

Max length4
Median length3
Mean length3.3490054
Min length3

Characters and Unicode

Total characters1676914
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174754
34.9%
1.0 449
 
0.1%

Length

2023-12-31T13:48:14.466289image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:14.626944image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174754
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174754
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676914
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174754
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676914
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174754
 
10.4%

crossbeam_product19_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
173775 
1.0
 
1428

Length

Max length4
Median length3
Mean length3.3470502
Min length3

Characters and Unicode

Total characters1675935
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 173775
34.7%
1.0 1428
 
0.3%

Length

2023-12-31T13:48:14.802762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:14.961051image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 173775
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.8%
Other Punctuation 500720
29.9%
Dash Punctuation 173775
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 173775
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1675935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 173775
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1675935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.5%
- 173775
 
10.4%

crossbeam_product20_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
167021 
1.0
 
8182

Length

Max length4
Median length3
Mean length3.3335617
Min length3

Characters and Unicode

Total characters1669181
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 167021
33.4%
1.0 8182
 
1.6%

Length

2023-12-31T13:48:15.137984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:15.297573image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.5%
. 500720
30.0%
1 175203
 
10.5%
- 167021
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
60.0%
Other Punctuation 500720
30.0%
Dash Punctuation 167021
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 167021
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1669181
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.5%
. 500720
30.0%
1 175203
 
10.5%
- 167021
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1669181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.5%
. 500720
30.0%
1 175203
 
10.5%
- 167021
 
10.0%

crossbeam_product21_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174429 
1.0
 
774

Length

Max length4
Median length3
Mean length3.3483564
Min length3

Characters and Unicode

Total characters1676589
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174429
34.8%
1.0 774
 
0.2%

Length

2023-12-31T13:48:15.472422image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:15.634527image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174429
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174429
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676589
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174429
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174429
 
10.4%

crossbeam_product22_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
174770 
1.0
 
433

Length

Max length4
Median length3
Mean length3.3490374
Min length3

Characters and Unicode

Total characters1676930
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 174770
34.9%
1.0 433
 
0.1%

Length

2023-12-31T13:48:15.814100image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:15.988481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174770
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
59.7%
Other Punctuation 500720
29.9%
Dash Punctuation 174770
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1676930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174770
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1676930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
49.3%
. 500720
29.9%
1 175203
 
10.4%
- 174770
 
10.4%

crossbeam_product23_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
0.0
325517 
-1.0
146789 
1.0
 
28414

Length

Max length4
Median length3
Mean length3.2931559
Min length3

Characters and Unicode

Total characters1648949
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 325517
65.0%
-1.0 146789
29.3%
1.0 28414
 
5.7%

Length

2023-12-31T13:48:16.168613image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:16.336376image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 325517
65.0%
1.0 175203
35.0%

Most occurring characters

ValueCountFrequency (%)
0 826237
50.1%
. 500720
30.4%
1 175203
 
10.6%
- 146789
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
60.7%
Other Punctuation 500720
30.4%
Dash Punctuation 146789
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826237
82.5%
1 175203
 
17.5%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 146789
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1648949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826237
50.1%
. 500720
30.4%
1 175203
 
10.6%
- 146789
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1648949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826237
50.1%
. 500720
30.4%
1 175203
 
10.6%
- 146789
 
8.9%

dnb_founded_time_grouped
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing296217
Missing (%)59.2%
Memory size35.0 MiB
After 2000
135200 
Before 2000
69303 

Length

Max length11
Median length10
Mean length10.338885
Min length10

Characters and Unicode

Total characters2114333
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBefore 2000
2nd rowAfter 2000
3rd rowAfter 2000
4th rowBefore 2000
5th rowBefore 2000

Common Values

ValueCountFrequency (%)
After 2000 135200
27.0%
Before 2000 69303
 
13.8%
(Missing) 296217
59.2%

Length

2023-12-31T13:48:16.611374image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:16.788655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
2000 204503
50.0%
after 135200
33.1%
before 69303
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 613509
29.0%
e 273806
12.9%
f 204503
 
9.7%
r 204503
 
9.7%
204503
 
9.7%
2 204503
 
9.7%
A 135200
 
6.4%
t 135200
 
6.4%
B 69303
 
3.3%
o 69303
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 887315
42.0%
Decimal Number 818012
38.7%
Space Separator 204503
 
9.7%
Uppercase Letter 204503
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 273806
30.9%
f 204503
23.0%
r 204503
23.0%
t 135200
15.2%
o 69303
 
7.8%
Decimal Number
ValueCountFrequency (%)
0 613509
75.0%
2 204503
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 135200
66.1%
B 69303
33.9%
Space Separator
ValueCountFrequency (%)
204503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1091818
51.6%
Common 1022515
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 273806
25.1%
f 204503
18.7%
r 204503
18.7%
A 135200
12.4%
t 135200
12.4%
B 69303
 
6.3%
o 69303
 
6.3%
Common
ValueCountFrequency (%)
0 613509
60.0%
204503
 
20.0%
2 204503
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2114333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 613509
29.0%
e 273806
12.9%
f 204503
 
9.7%
r 204503
 
9.7%
204503
 
9.7%
2 204503
 
9.7%
A 135200
 
6.4%
t 135200
 
6.4%
B 69303
 
3.3%
o 69303
 
3.3%

has_hg_data
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
454325 
True
46395 
ValueCountFrequency (%)
False 454325
90.7%
True 46395
 
9.3%
2023-12-31T13:48:16.938079image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

hg_product_27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47559 
1.0
 
1

Length

Max length4
Median length3
Mean length3.0949812
Min length3

Characters and Unicode

Total characters1549719
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47559
 
9.5%
1.0 1
 
< 0.1%

Length

2023-12-31T13:48:17.105293image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:17.260254image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47559
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47559
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47559
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47559
 
3.1%

hg_product_28
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44286
1.0
 
3274

Length

Max length4
Median length3
Mean length3.0884446
Min length3

Characters and Unicode

Total characters1546446
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44286
 
8.8%
1.0 3274
 
0.7%

Length

2023-12-31T13:48:17.435423image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:17.598019image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44286
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44286
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44286
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44286
 
2.9%

hg_product_29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46801 
1.0
 
759

Length

Max length4
Median length3
Mean length3.0934674
Min length3

Characters and Unicode

Total characters1548961
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46801
 
9.3%
1.0 759
 
0.2%

Length

2023-12-31T13:48:17.769348image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:17.924879image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46801
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46801
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46801
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46801
 
3.0%

hg_product_30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46921 
1.0
 
639

Length

Max length4
Median length3
Mean length3.0937071
Min length3

Characters and Unicode

Total characters1549081
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46921
 
9.4%
1.0 639
 
0.1%

Length

2023-12-31T13:48:18.105023image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:18.264092image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46921
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 46921
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549081
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46921
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46921
 
3.0%

hg_product_31
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
41325
1.0
 
6235

Length

Max length4
Median length3
Mean length3.0825312
Min length3

Characters and Unicode

Total characters1543485
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 41325
 
8.3%
1.0 6235
 
1.2%

Length

2023-12-31T13:48:18.437368image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:18.593369image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41325
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.4%
Dash Punctuation 41325
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1543485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41325
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1543485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41325
 
2.7%

hg_product_32
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
30586
1.0
 
16974

Length

Max length4
Median length3
Mean length3.061084
Min length3

Characters and Unicode

Total characters1532746
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 30586
 
6.1%
1.0 16974
 
3.4%

Length

2023-12-31T13:48:18.774798image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:18.939288image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.2%
. 500720
32.7%
1 47560
 
3.1%
- 30586
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.3%
Other Punctuation 500720
32.7%
Dash Punctuation 30586
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1532746
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.2%
. 500720
32.7%
1 47560
 
3.1%
- 30586
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1532746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.2%
. 500720
32.7%
1 47560
 
3.1%
- 30586
 
2.0%

hg_product_33
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
38215
1.0
 
9345

Length

Max length4
Median length3
Mean length3.0763201
Min length3

Characters and Unicode

Total characters1540375
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 38215
 
7.6%
1.0 9345
 
1.9%

Length

2023-12-31T13:48:19.122526image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:19.283527image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 38215
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.0%
Other Punctuation 500720
32.5%
Dash Punctuation 38215
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1540375
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 38215
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1540375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 38215
 
2.5%

hg_product_34
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
37710
1.0
 
9850

Length

Max length4
Median length3
Mean length3.0753116
Min length3

Characters and Unicode

Total characters1539870
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 37710
 
7.5%
1.0 9850
 
2.0%

Length

2023-12-31T13:48:19.458454image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:19.619280image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 37710
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.0%
Other Punctuation 500720
32.5%
Dash Punctuation 37710
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1539870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 37710
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1539870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 37710
 
2.4%

hg_product_35
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47251 
1.0
 
309

Length

Max length4
Median length3
Mean length3.0943661
Min length3

Characters and Unicode

Total characters1549411
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47251
 
9.4%
1.0 309
 
0.1%

Length

2023-12-31T13:48:19.788861image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:19.942873image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47251
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47251
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47251
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47251
 
3.0%

hg_product_36
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
43457
1.0
 
4103

Length

Max length4
Median length3
Mean length3.086789
Min length3

Characters and Unicode

Total characters1545617
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 43457
 
8.7%
1.0 4103
 
0.8%

Length

2023-12-31T13:48:20.114521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:20.274472image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43457
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 43457
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545617
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43457
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43457
 
2.8%

hg_product_37
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44945
1.0
 
2615

Length

Max length4
Median length3
Mean length3.0897607
Min length3

Characters and Unicode

Total characters1547105
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44945
 
9.0%
1.0 2615
 
0.5%

Length

2023-12-31T13:48:20.444938image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:20.602384image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44945
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 44945
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44945
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547105
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44945
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44945
 
2.9%

hg_product_38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
41625
1.0
 
5935

Length

Max length4
Median length3
Mean length3.0831303
Min length3

Characters and Unicode

Total characters1543785
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 41625
 
8.3%
1.0 5935
 
1.2%

Length

2023-12-31T13:48:20.773342image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:20.929843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41625
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.4%
Dash Punctuation 41625
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41625
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1543785
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41625
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1543785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41625
 
2.7%

hg_product_39
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
42878
1.0
 
4682

Length

Max length4
Median length3
Mean length3.0856327
Min length3

Characters and Unicode

Total characters1545038
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 42878
 
8.6%
1.0 4682
 
0.9%

Length

2023-12-31T13:48:21.103781image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:21.260773image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 42878
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 42878
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42878
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 42878
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 42878
 
2.8%

hg_product_40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47397 
1.0
 
163

Length

Max length4
Median length3
Mean length3.0946577
Min length3

Characters and Unicode

Total characters1549557
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47397
 
9.5%
1.0 163
 
< 0.1%

Length

2023-12-31T13:48:21.433199image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:21.589913image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47397
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47397
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47397
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549557
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47397
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47397
 
3.1%

hg_product_41
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44007
1.0
 
3553

Length

Max length4
Median length3
Mean length3.0878874
Min length3

Characters and Unicode

Total characters1546167
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44007
 
8.8%
1.0 3553
 
0.7%

Length

2023-12-31T13:48:21.759140image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:21.915174image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44007
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44007
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546167
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44007
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44007
 
2.8%

hg_product_42
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44033
1.0
 
3527

Length

Max length4
Median length3
Mean length3.0879394
Min length3

Characters and Unicode

Total characters1546193
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44033
 
8.8%
1.0 3527
 
0.7%

Length

2023-12-31T13:48:22.091601image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:22.252793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44033
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44033
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44033
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44033
 
2.8%

hg_product_43
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46889 
1.0
 
671

Length

Max length4
Median length3
Mean length3.0936432
Min length3

Characters and Unicode

Total characters1549049
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46889
 
9.4%
1.0 671
 
0.1%

Length

2023-12-31T13:48:22.423961image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:22.585195image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46889
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 46889
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46889
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549049
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46889
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46889
 
3.0%

hg_product_44
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
43297
1.0
 
4263

Length

Max length4
Median length3
Mean length3.0864695
Min length3

Characters and Unicode

Total characters1545457
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 43297
 
8.6%
1.0 4263
 
0.9%

Length

2023-12-31T13:48:22.758956image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:22.917475image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43297
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 43297
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43297
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43297
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43297
 
2.8%

hg_product_45
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
41940
1.0
 
5620

Length

Max length4
Median length3
Mean length3.0837594
Min length3

Characters and Unicode

Total characters1544100
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 41940
 
8.4%
1.0 5620
 
1.1%

Length

2023-12-31T13:48:23.094597image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:23.253515image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41940
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.4%
Dash Punctuation 41940
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1544100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41940
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41940
 
2.7%

hg_product_46
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47471 
1.0
 
89

Length

Max length4
Median length3
Mean length3.0948055
Min length3

Characters and Unicode

Total characters1549631
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47471
 
9.5%
1.0 89
 
< 0.1%

Length

2023-12-31T13:48:23.423588image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:23.581777image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47471
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47471
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47471
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549631
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47471
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47471
 
3.1%

hg_product_47
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
33060
1.0
 
14500

Length

Max length4
Median length3
Mean length3.0660249
Min length3

Characters and Unicode

Total characters1535220
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 33060
 
6.6%
1.0 14500
 
2.9%

Length

2023-12-31T13:48:23.753666image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:23.905852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33060
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.2%
Other Punctuation 500720
32.6%
Dash Punctuation 33060
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1535220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33060
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1535220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33060
 
2.2%

hg_product_48
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
1.0
 
29009
-1.0
 
18551

Length

Max length4
Median length3
Mean length3.0370486
Min length3

Characters and Unicode

Total characters1520711
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
1.0 29009
 
5.8%
-1.0 18551
 
3.7%

Length

2023-12-31T13:48:24.083913image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:24.246032image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.7%
. 500720
32.9%
1 47560
 
3.1%
- 18551
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.9%
Other Punctuation 500720
32.9%
Dash Punctuation 18551
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1520711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.7%
. 500720
32.9%
1 47560
 
3.1%
- 18551
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1520711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.7%
. 500720
32.9%
1 47560
 
3.1%
- 18551
 
1.2%

hg_product_49
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46008 
1.0
 
1552

Length

Max length4
Median length3
Mean length3.0918837
Min length3

Characters and Unicode

Total characters1548168
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46008
 
9.2%
1.0 1552
 
0.3%

Length

2023-12-31T13:48:24.421245image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:24.574756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46008
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46008
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46008
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46008
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46008
 
3.0%

hg_product_50
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
41672
1.0
 
5888

Length

Max length4
Median length3
Mean length3.0832242
Min length3

Characters and Unicode

Total characters1543832
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 41672
 
8.3%
1.0 5888
 
1.2%

Length

2023-12-31T13:48:24.745227image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:24.904339image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41672
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.4%
Dash Punctuation 41672
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1543832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41672
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1543832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41672
 
2.7%

hg_product_51
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
45497 
1.0
 
2063

Length

Max length4
Median length3
Mean length3.0908632
Min length3

Characters and Unicode

Total characters1547657
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45497
 
9.1%
1.0 2063
 
0.4%

Length

2023-12-31T13:48:25.077190image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:25.444181image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45497
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 45497
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45497
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547657
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45497
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45497
 
2.9%

hg_product_52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46170 
1.0
 
1390

Length

Max length4
Median length3
Mean length3.0922072
Min length3

Characters and Unicode

Total characters1548330
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46170
 
9.2%
1.0 1390
 
0.3%

Length

2023-12-31T13:48:25.619740image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:25.781241image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46170
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46170
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46170
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46170
 
3.0%

hg_product_53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46816 
1.0
 
744

Length

Max length4
Median length3
Mean length3.0934974
Min length3

Characters and Unicode

Total characters1548976
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46816
 
9.3%
1.0 744
 
0.1%

Length

2023-12-31T13:48:25.956526image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:26.115075image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46816
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46816
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46816
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46816
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46816
 
3.0%

hg_product_54
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47526 
1.0
 
34

Length

Max length4
Median length3
Mean length3.0949153
Min length3

Characters and Unicode

Total characters1549686
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47526
 
9.5%
1.0 34
 
< 0.1%

Length

2023-12-31T13:48:26.288923image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:26.445288image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47526
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47526
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47526
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47526
 
3.1%

hg_product_55
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
38578
1.0
 
8982

Length

Max length4
Median length3
Mean length3.0770451
Min length3

Characters and Unicode

Total characters1540738
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 38578
 
7.7%
1.0 8982
 
1.8%

Length

2023-12-31T13:48:26.619050image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:26.773321image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 38578
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.0%
Other Punctuation 500720
32.5%
Dash Punctuation 38578
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38578
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1540738
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 38578
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1540738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.9%
. 500720
32.5%
1 47560
 
3.1%
- 38578
 
2.5%

hg_product_56
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47496 
1.0
 
64

Length

Max length4
Median length3
Mean length3.0948554
Min length3

Characters and Unicode

Total characters1549656
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47496
 
9.5%
1.0 64
 
< 0.1%

Length

2023-12-31T13:48:26.938789image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:27.098618image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47496
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47496
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47496
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47496
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47496
 
3.1%

hg_product_57
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44449
1.0
 
3111

Length

Max length4
Median length3
Mean length3.0887702
Min length3

Characters and Unicode

Total characters1546609
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44449
 
8.9%
1.0 3111
 
0.6%

Length

2023-12-31T13:48:27.272329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:27.430273image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44449
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44449
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44449
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44449
 
2.9%

hg_product_58
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47541 
1.0
 
19

Length

Max length4
Median length3
Mean length3.0949453
Min length3

Characters and Unicode

Total characters1549701
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47541
 
9.5%
1.0 19
 
< 0.1%

Length

2023-12-31T13:48:27.601499image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:27.759306image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47541
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47541
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47541
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549701
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47541
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47541
 
3.1%

hg_product_59
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47552 
1.0
 
8

Length

Max length4
Median length3
Mean length3.0949672
Min length3

Characters and Unicode

Total characters1549712
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47552
 
9.5%
1.0 8
 
< 0.1%

Length

2023-12-31T13:48:27.942829image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:28.113849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47552
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47552
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47552
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47552
 
3.1%

hg_product_60
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47368 
1.0
 
192

Length

Max length4
Median length3
Mean length3.0945998
Min length3

Characters and Unicode

Total characters1549528
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47368
 
9.5%
1.0 192
 
< 0.1%

Length

2023-12-31T13:48:28.295577image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:28.455988image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47368
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47368
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47368
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47368
 
3.1%

hg_product_61
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47537 
1.0
 
23

Length

Max length4
Median length3
Mean length3.0949373
Min length3

Characters and Unicode

Total characters1549697
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47537
 
9.5%
1.0 23
 
< 0.1%

Length

2023-12-31T13:48:28.632614image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:28.801849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47537
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47537
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47537
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47537
 
3.1%

hg_product_62
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44866
1.0
 
2694

Length

Max length4
Median length3
Mean length3.089603
Min length3

Characters and Unicode

Total characters1547026
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44866
 
9.0%
1.0 2694
 
0.5%

Length

2023-12-31T13:48:28.974231image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:29.137842image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44866
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 44866
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44866
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547026
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44866
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44866
 
2.9%

hg_product_63
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
41252
1.0
 
6308

Length

Max length4
Median length3
Mean length3.0823854
Min length3

Characters and Unicode

Total characters1543412
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 41252
 
8.2%
1.0 6308
 
1.3%

Length

2023-12-31T13:48:29.311738image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:29.468905image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41252
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.4%
Dash Punctuation 41252
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1543412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41252
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1543412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41252
 
2.7%

hg_product_64
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47435 
1.0
 
125

Length

Max length4
Median length3
Mean length3.0947336
Min length3

Characters and Unicode

Total characters1549595
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47435
 
9.5%
1.0 125
 
< 0.1%

Length

2023-12-31T13:48:29.642193image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:29.797594image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47435
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47435
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549595
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47435
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47435
 
3.1%

hg_product_65
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
45063
1.0
 
2497

Length

Max length4
Median length3
Mean length3.0899964
Min length3

Characters and Unicode

Total characters1547223
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45063
 
9.0%
1.0 2497
 
0.5%

Length

2023-12-31T13:48:29.970706image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:30.127458image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 45063
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 45063
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45063
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 45063
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 45063
 
2.9%

hg_product_66
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47068 
1.0
 
492

Length

Max length4
Median length3
Mean length3.0940006
Min length3

Characters and Unicode

Total characters1549228
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47068
 
9.4%
1.0 492
 
0.1%

Length

2023-12-31T13:48:30.301271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:30.465995image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47068
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47068
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47068
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47068
 
3.0%

hg_product_67
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
28465
1.0
 
19095

Length

Max length4
Median length3
Mean length3.0568481
Min length3

Characters and Unicode

Total characters1530625
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 28465
 
5.7%
1.0 19095
 
3.8%

Length

2023-12-31T13:48:30.640389image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:30.804152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 28465
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.4%
Other Punctuation 500720
32.7%
Dash Punctuation 28465
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28465
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1530625
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 28465
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1530625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 28465
 
1.9%

hg_product_68
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
43418
1.0
 
4142

Length

Max length4
Median length3
Mean length3.0867111
Min length3

Characters and Unicode

Total characters1545578
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 43418
 
8.7%
1.0 4142
 
0.8%

Length

2023-12-31T13:48:30.973577image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:31.131739image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43418
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 43418
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43418
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43418
 
2.8%

hg_product_69
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
42431
1.0
 
5129

Length

Max length4
Median length3
Mean length3.08474
Min length3

Characters and Unicode

Total characters1544591
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 42431
 
8.5%
1.0 5129
 
1.0%

Length

2023-12-31T13:48:31.307171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:31.461964image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42431
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 42431
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1544591
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42431
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42431
 
2.7%

hg_product_70
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46348 
1.0
 
1212

Length

Max length4
Median length3
Mean length3.0925627
Min length3

Characters and Unicode

Total characters1548508
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46348
 
9.3%
1.0 1212
 
0.2%

Length

2023-12-31T13:48:31.635064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:31.790657image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46348
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46348
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46348
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46348
 
3.0%

hg_product_71
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47076 
1.0
 
484

Length

Max length4
Median length3
Mean length3.0940166
Min length3

Characters and Unicode

Total characters1549236
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47076
 
9.4%
1.0 484
 
0.1%

Length

2023-12-31T13:48:31.959695image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:32.116854image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47076
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47076
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47076
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47076
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47076
 
3.0%

hg_product_72
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44720
1.0
 
2840

Length

Max length4
Median length3
Mean length3.0893114
Min length3

Characters and Unicode

Total characters1546880
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44720
 
8.9%
1.0 2840
 
0.6%

Length

2023-12-31T13:48:32.285800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:32.443537image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44720
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 44720
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44720
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44720
 
2.9%

hg_product_73
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
24236
1.0
 
23324

Length

Max length4
Median length3
Mean length3.0484023
Min length3

Characters and Unicode

Total characters1526396
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 24236
 
4.8%
1.0 23324
 
4.7%

Length

2023-12-31T13:48:32.655755image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:32.813743image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.5%
. 500720
32.8%
1 47560
 
3.1%
- 24236
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.6%
Other Punctuation 500720
32.8%
Dash Punctuation 24236
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1526396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.5%
. 500720
32.8%
1 47560
 
3.1%
- 24236
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1526396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.5%
. 500720
32.8%
1 47560
 
3.1%
- 24236
 
1.6%

hg_product_74
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
45410 
1.0
 
2150

Length

Max length4
Median length3
Mean length3.0906894
Min length3

Characters and Unicode

Total characters1547570
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45410
 
9.1%
1.0 2150
 
0.4%

Length

2023-12-31T13:48:32.983513image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:33.141179image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45410
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 45410
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45410
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45410
 
2.9%

hg_product_75
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44289
1.0
 
3271

Length

Max length4
Median length3
Mean length3.0884506
Min length3

Characters and Unicode

Total characters1546449
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44289
 
8.8%
1.0 3271
 
0.7%

Length

2023-12-31T13:48:33.317021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:33.473822image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44289
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44289
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546449
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44289
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44289
 
2.9%

hg_product_76
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
42462
1.0
 
5098

Length

Max length4
Median length3
Mean length3.0848019
Min length3

Characters and Unicode

Total characters1544622
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 42462
 
8.5%
1.0 5098
 
1.0%

Length

2023-12-31T13:48:33.643960image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:33.801845image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42462
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 42462
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1544622
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42462
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42462
 
2.7%

hg_product_77
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
42349
1.0
 
5211

Length

Max length4
Median length3
Mean length3.0845762
Min length3

Characters and Unicode

Total characters1544509
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 42349
 
8.5%
1.0 5211
 
1.0%

Length

2023-12-31T13:48:33.971171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:34.137607image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42349
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 42349
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1544509
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42349
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42349
 
2.7%

hg_product_78
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
45883 
1.0
 
1677

Length

Max length4
Median length3
Mean length3.091634
Min length3

Characters and Unicode

Total characters1548043
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45883
 
9.2%
1.0 1677
 
0.3%

Length

2023-12-31T13:48:34.316530image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:34.471546image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45883
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 45883
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45883
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45883
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45883
 
3.0%

hg_product_79
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44142
1.0
 
3418

Length

Max length4
Median length3
Mean length3.0881571
Min length3

Characters and Unicode

Total characters1546302
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44142
 
8.8%
1.0 3418
 
0.7%

Length

2023-12-31T13:48:34.643427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:34.804664image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44142
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44142
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44142
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44142
 
2.9%

hg_product_80
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46047 
1.0
 
1513

Length

Max length4
Median length3
Mean length3.0919616
Min length3

Characters and Unicode

Total characters1548207
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46047
 
9.2%
1.0 1513
 
0.3%

Length

2023-12-31T13:48:34.984181image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:35.148321image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46047
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46047
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46047
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46047
 
3.0%

hg_product_81
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44775
1.0
 
2785

Length

Max length4
Median length3
Mean length3.0894212
Min length3

Characters and Unicode

Total characters1546935
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44775
 
8.9%
1.0 2785
 
0.6%

Length

2023-12-31T13:48:35.321261image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:35.487823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44775
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 44775
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44775
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44775
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44775
 
2.9%

hg_product_82
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47554 
1.0
 
6

Length

Max length4
Median length3
Mean length3.0949712
Min length3

Characters and Unicode

Total characters1549714
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47554
 
9.5%
1.0 6
 
< 0.1%

Length

2023-12-31T13:48:35.663531image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:35.820794image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47554
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47554
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47554
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47554
 
3.1%

hg_product_83
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
35716
1.0
 
11844

Length

Max length4
Median length3
Mean length3.0713293
Min length3

Characters and Unicode

Total characters1537876
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 35716
 
7.1%
1.0 11844
 
2.4%

Length

2023-12-31T13:48:36.011433image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:36.171551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.6%
1 47560
 
3.1%
- 35716
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.1%
Other Punctuation 500720
32.6%
Dash Punctuation 35716
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1537876
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.6%
1 47560
 
3.1%
- 35716
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1537876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.6%
1 47560
 
3.1%
- 35716
 
2.3%

hg_product_84
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46897 
1.0
 
663

Length

Max length4
Median length3
Mean length3.0936591
Min length3

Characters and Unicode

Total characters1549057
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46897
 
9.4%
1.0 663
 
0.1%

Length

2023-12-31T13:48:36.342271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:36.498916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46897
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 46897
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549057
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46897
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46897
 
3.0%

hg_product_85
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47552 
1.0
 
8

Length

Max length4
Median length3
Mean length3.0949672
Min length3

Characters and Unicode

Total characters1549712
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47552
 
9.5%
1.0 8
 
< 0.1%

Length

2023-12-31T13:48:36.668003image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:36.827227image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47552
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47552
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47552
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47552
 
3.1%

hg_product_86
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46939 
1.0
 
621

Length

Max length4
Median length3
Mean length3.093743
Min length3

Characters and Unicode

Total characters1549099
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46939
 
9.4%
1.0 621
 
0.1%

Length

2023-12-31T13:48:36.996943image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:37.156312image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46939
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 46939
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46939
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46939
 
3.0%

hg_product_87
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
29550
1.0
 
18010

Length

Max length4
Median length3
Mean length3.059015
Min length3

Characters and Unicode

Total characters1531710
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 29550
 
5.9%
1.0 18010
 
3.6%

Length

2023-12-31T13:48:37.327375image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:37.483617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 29550
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.4%
Other Punctuation 500720
32.7%
Dash Punctuation 29550
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1531710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 29550
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1531710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 29550
 
1.9%

hg_product_88
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
43583
1.0
 
3977

Length

Max length4
Median length3
Mean length3.0870407
Min length3

Characters and Unicode

Total characters1545743
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 43583
 
8.7%
1.0 3977
 
0.8%

Length

2023-12-31T13:48:37.653329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:37.811894image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43583
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 43583
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545743
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43583
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43583
 
2.8%

hg_product_89
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46676 
1.0
 
884

Length

Max length4
Median length3
Mean length3.0932178
Min length3

Characters and Unicode

Total characters1548836
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46676
 
9.3%
1.0 884
 
0.2%

Length

2023-12-31T13:48:37.987114image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:38.144048image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46676
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46676
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46676
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46676
 
3.0%

hg_product_90
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46417 
1.0
 
1143

Length

Max length4
Median length3
Mean length3.0927005
Min length3

Characters and Unicode

Total characters1548577
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46417
 
9.3%
1.0 1143
 
0.2%

Length

2023-12-31T13:48:38.314750image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:38.475770image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46417
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46417
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46417
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46417
 
3.0%

hg_product_91
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47546 
1.0
 
14

Length

Max length4
Median length3
Mean length3.0949553
Min length3

Characters and Unicode

Total characters1549706
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47546
 
9.5%
1.0 14
 
< 0.1%

Length

2023-12-31T13:48:38.663493image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:38.827181image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47546
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47546
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549706
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47546
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47546
 
3.1%

hg_product_92
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47136 
1.0
 
424

Length

Max length4
Median length3
Mean length3.0941364
Min length3

Characters and Unicode

Total characters1549296
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47136
 
9.4%
1.0 424
 
0.1%

Length

2023-12-31T13:48:39.009968image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:39.171057image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47136
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47136
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47136
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47136
 
3.0%

hg_product_93
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47558 
1.0
 
2

Length

Max length4
Median length3
Mean length3.0949792
Min length3

Characters and Unicode

Total characters1549718
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47558
 
9.5%
1.0 2
 
< 0.1%

Length

2023-12-31T13:48:39.346330image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:39.505201image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47558
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47558
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549718
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47558
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47558
 
3.1%

hg_product_94
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47472 
1.0
 
88

Length

Max length4
Median length3
Mean length3.0948075
Min length3

Characters and Unicode

Total characters1549632
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47472
 
9.5%
1.0 88
 
< 0.1%

Length

2023-12-31T13:48:39.678734image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:39.841734image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47472
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47472
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549632
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47472
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47472
 
3.1%

hg_product_95
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
45781 
1.0
 
1779

Length

Max length4
Median length3
Mean length3.0914303
Min length3

Characters and Unicode

Total characters1547941
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45781
 
9.1%
1.0 1779
 
0.4%

Length

2023-12-31T13:48:40.014269image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:40.430078image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45781
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 45781
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45781
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45781
 
3.0%

hg_product_96
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44183
1.0
 
3377

Length

Max length4
Median length3
Mean length3.0882389
Min length3

Characters and Unicode

Total characters1546343
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44183
 
8.8%
1.0 3377
 
0.7%

Length

2023-12-31T13:48:40.610372image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:40.808325image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44183
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44183
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44183
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44183
 
2.9%

hg_product_97
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47309 
1.0
 
251

Length

Max length4
Median length3
Mean length3.0944819
Min length3

Characters and Unicode

Total characters1549469
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47309
 
9.4%
1.0 251
 
0.1%

Length

2023-12-31T13:48:41.000020image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:41.159078image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47309
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47309
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47309
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47309
 
3.1%

hg_product_98
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47089 
1.0
 
471

Length

Max length4
Median length3
Mean length3.0940426
Min length3

Characters and Unicode

Total characters1549249
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47089
 
9.4%
1.0 471
 
0.1%

Length

2023-12-31T13:48:41.334161image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:41.516721image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47089
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47089
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549249
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47089
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47089
 
3.0%

hg_product_99
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47129 
1.0
 
431

Length

Max length4
Median length3
Mean length3.0941225
Min length3

Characters and Unicode

Total characters1549289
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47129
 
9.4%
1.0 431
 
0.1%

Length

2023-12-31T13:48:41.693245image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:41.869292image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47129
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47129
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549289
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47129
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47129
 
3.0%

hg_product_100
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46388 
1.0
 
1172

Length

Max length4
Median length3
Mean length3.0926426
Min length3

Characters and Unicode

Total characters1548548
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46388
 
9.3%
1.0 1172
 
0.2%

Length

2023-12-31T13:48:42.041535image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:42.218704image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46388
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46388
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46388
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46388
 
3.0%

hg_product_101
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46703 
1.0
 
857

Length

Max length4
Median length3
Mean length3.0932717
Min length3

Characters and Unicode

Total characters1548863
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46703
 
9.3%
1.0 857
 
0.2%

Length

2023-12-31T13:48:42.410415image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:42.574419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46703
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46703
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46703
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548863
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46703
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46703
 
3.0%

hg_product_102
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
35603
1.0
 
11957

Length

Max length4
Median length3
Mean length3.0711036
Min length3

Characters and Unicode

Total characters1537763
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 35603
 
7.1%
1.0 11957
 
2.4%

Length

2023-12-31T13:48:42.765519image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:42.929771image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.6%
1 47560
 
3.1%
- 35603
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.1%
Other Punctuation 500720
32.6%
Dash Punctuation 35603
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1537763
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.6%
1 47560
 
3.1%
- 35603
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1537763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.6%
1 47560
 
3.1%
- 35603
 
2.3%

hg_product_103
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
43511
1.0
 
4049

Length

Max length4
Median length3
Mean length3.0868969
Min length3

Characters and Unicode

Total characters1545671
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 43511
 
8.7%
1.0 4049
 
0.8%

Length

2023-12-31T13:48:43.102128image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:43.269129image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43511
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 43511
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545671
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43511
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43511
 
2.8%

hg_product_104
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46981 
1.0
 
579

Length

Max length4
Median length3
Mean length3.0938269
Min length3

Characters and Unicode

Total characters1549141
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46981
 
9.4%
1.0 579
 
0.1%

Length

2023-12-31T13:48:43.460549image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:43.652460image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46981
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 46981
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46981
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46981
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46981
 
3.0%

hg_product_105
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44853
1.0
 
2707

Length

Max length4
Median length3
Mean length3.089577
Min length3

Characters and Unicode

Total characters1547013
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44853
 
9.0%
1.0 2707
 
0.5%

Length

2023-12-31T13:48:43.856854image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:44.026476image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44853
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 44853
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44853
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44853
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44853
 
2.9%

hg_product_106
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47378 
1.0
 
182

Length

Max length4
Median length3
Mean length3.0946197
Min length3

Characters and Unicode

Total characters1549538
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47378
 
9.5%
1.0 182
 
< 0.1%

Length

2023-12-31T13:48:44.208970image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:44.382050image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47378
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47378
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47378
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47378
 
3.1%

hg_product_107
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47321 
1.0
 
239

Length

Max length4
Median length3
Mean length3.0945059
Min length3

Characters and Unicode

Total characters1549481
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47321
 
9.5%
1.0 239
 
< 0.1%

Length

2023-12-31T13:48:44.561570image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:44.724650image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47321
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47321
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549481
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47321
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47321
 
3.1%

hg_product_108
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47556 
1.0
 
4

Length

Max length4
Median length3
Mean length3.0949752
Min length3

Characters and Unicode

Total characters1549716
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47556
 
9.5%
1.0 4
 
< 0.1%

Length

2023-12-31T13:48:44.907418image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:45.071604image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47556
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47556
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549716
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47556
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47556
 
3.1%

hg_product_109
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47510 
1.0
 
50

Length

Max length4
Median length3
Mean length3.0948834
Min length3

Characters and Unicode

Total characters1549670
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47510
 
9.5%
1.0 50
 
< 0.1%

Length

2023-12-31T13:48:45.260887image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:45.444674image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47510
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47510
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47510
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47510
 
3.1%

hg_product_110
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47432 
1.0
 
128

Length

Max length4
Median length3
Mean length3.0947276
Min length3

Characters and Unicode

Total characters1549592
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47432
 
9.5%
1.0 128
 
< 0.1%

Length

2023-12-31T13:48:45.622439image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:45.785482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47432
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47432
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47432
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47432
 
3.1%

hg_product_111
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47540 
1.0
 
20

Length

Max length4
Median length3
Mean length3.0949433
Min length3

Characters and Unicode

Total characters1549700
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47540
 
9.5%
1.0 20
 
< 0.1%

Length

2023-12-31T13:48:45.965632image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:46.127937image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47540
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47540
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47540
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47540
 
3.1%

hg_product_112
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
45506 
1.0
 
2054

Length

Max length4
Median length3
Mean length3.0908811
Min length3

Characters and Unicode

Total characters1547666
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45506
 
9.1%
1.0 2054
 
0.4%

Length

2023-12-31T13:48:46.309585image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:46.472407image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45506
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 45506
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547666
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45506
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.4%
1 47560
 
3.1%
- 45506
 
2.9%

hg_product_113
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47544 
1.0
 
16

Length

Max length4
Median length3
Mean length3.0949513
Min length3

Characters and Unicode

Total characters1549704
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47544
 
9.5%
1.0 16
 
< 0.1%

Length

2023-12-31T13:48:46.651728image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:46.811506image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47544
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47544
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47544
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47544
 
3.1%

hg_product_114
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
45684 
1.0
 
1876

Length

Max length4
Median length3
Mean length3.0912366
Min length3

Characters and Unicode

Total characters1547844
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 45684
 
9.1%
1.0 1876
 
0.4%

Length

2023-12-31T13:48:46.986635image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:47.156576image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45684
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 45684
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45684
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 45684
 
3.0%

hg_product_115
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47491 
1.0
 
69

Length

Max length4
Median length3
Mean length3.0948454
Min length3

Characters and Unicode

Total characters1549651
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47491
 
9.5%
1.0 69
 
< 0.1%

Length

2023-12-31T13:48:47.358478image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:47.533376image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47491
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47491
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47491
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47491
 
3.1%

hg_product_116
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47116 
1.0
 
444

Length

Max length4
Median length3
Mean length3.0940965
Min length3

Characters and Unicode

Total characters1549276
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47116
 
9.4%
1.0 444
 
0.1%

Length

2023-12-31T13:48:47.723953image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:47.889831image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47116
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47116
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549276
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47116
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47116
 
3.0%

hg_product_117
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47425 
1.0
 
135

Length

Max length4
Median length3
Mean length3.0947136
Min length3

Characters and Unicode

Total characters1549585
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47425
 
9.5%
1.0 135
 
< 0.1%

Length

2023-12-31T13:48:48.067297image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:48.228632image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47425
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47425
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47425
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549585
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47425
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47425
 
3.1%

hg_product_118
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47525 
1.0
 
35

Length

Max length4
Median length3
Mean length3.0949133
Min length3

Characters and Unicode

Total characters1549685
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47525
 
9.5%
1.0 35
 
< 0.1%

Length

2023-12-31T13:48:48.428449image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:48.617944image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47525
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47525
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47525
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47525
 
3.1%

hg_product_119
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44694
1.0
 
2866

Length

Max length4
Median length3
Mean length3.0892595
Min length3

Characters and Unicode

Total characters1546854
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44694
 
8.9%
1.0 2866
 
0.6%

Length

2023-12-31T13:48:48.814419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:48.977804image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44694
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.4%
Dash Punctuation 44694
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44694
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44694
 
2.9%

hg_product_120
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47151 
1.0
 
409

Length

Max length4
Median length3
Mean length3.0941664
Min length3

Characters and Unicode

Total characters1549311
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47151
 
9.4%
1.0 409
 
0.1%

Length

2023-12-31T13:48:49.148980image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:49.308217image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47151
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47151
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47151
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47151
 
3.0%

hg_product_121
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47489 
1.0
 
71

Length

Max length4
Median length3
Mean length3.0948414
Min length3

Characters and Unicode

Total characters1549649
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47489
 
9.5%
1.0 71
 
< 0.1%

Length

2023-12-31T13:48:49.480037image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:49.642898image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47489
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47489
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47489
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47489
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47489
 
3.1%

hg_product_122
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47551 
1.0
 
9

Length

Max length4
Median length3
Mean length3.0949653
Min length3

Characters and Unicode

Total characters1549711
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47551
 
9.5%
1.0 9
 
< 0.1%

Length

2023-12-31T13:48:49.818539image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:49.976942image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47551
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47551
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47551
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47551
 
3.1%

hg_product_123
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46677 
1.0
 
883

Length

Max length4
Median length3
Mean length3.0932198
Min length3

Characters and Unicode

Total characters1548837
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46677
 
9.3%
1.0 883
 
0.2%

Length

2023-12-31T13:48:50.153616image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:50.311486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46677
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46677
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548837
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46677
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46677
 
3.0%

hg_product_124
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
36822
1.0
 
10738

Length

Max length4
Median length3
Mean length3.0735381
Min length3

Characters and Unicode

Total characters1538982
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 36822
 
7.4%
1.0 10738
 
2.1%

Length

2023-12-31T13:48:50.496136image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:50.664389image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.5%
1 47560
 
3.1%
- 36822
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.1%
Other Punctuation 500720
32.5%
Dash Punctuation 36822
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36822
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1538982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.5%
1 47560
 
3.1%
- 36822
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1538982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.0%
. 500720
32.5%
1 47560
 
3.1%
- 36822
 
2.4%

hg_product_125
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
33409
1.0
 
14151

Length

Max length4
Median length3
Mean length3.0667219
Min length3

Characters and Unicode

Total characters1535569
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 33409
 
6.7%
1.0 14151
 
2.8%

Length

2023-12-31T13:48:50.847843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:51.015888image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33409
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.2%
Other Punctuation 500720
32.6%
Dash Punctuation 33409
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1535569
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33409
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1535569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33409
 
2.2%

hg_product_126
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46955 
1.0
 
605

Length

Max length4
Median length3
Mean length3.093775
Min length3

Characters and Unicode

Total characters1549115
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46955
 
9.4%
1.0 605
 
0.1%

Length

2023-12-31T13:48:51.194927image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:51.361918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46955
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 46955
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46955
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46955
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46955
 
3.0%

hg_product_127
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47092 
1.0
 
468

Length

Max length4
Median length3
Mean length3.0940486
Min length3

Characters and Unicode

Total characters1549252
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47092
 
9.4%
1.0 468
 
0.1%

Length

2023-12-31T13:48:51.546523image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:51.708896image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47092
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47092
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47092
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47092
 
3.0%

hg_product_128
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47535 
1.0
 
25

Length

Max length4
Median length3
Mean length3.0949333
Min length3

Characters and Unicode

Total characters1549695
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47535
 
9.5%
1.0 25
 
< 0.1%

Length

2023-12-31T13:48:51.885212image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:52.043521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47535
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47535
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47535
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47535
 
3.1%

hg_product_129
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
28210
1.0
 
19350

Length

Max length4
Median length3
Mean length3.0563389
Min length3

Characters and Unicode

Total characters1530370
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 28210
 
5.6%
1.0 19350
 
3.9%

Length

2023-12-31T13:48:52.216732image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:52.387365image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 28210
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.4%
Other Punctuation 500720
32.7%
Dash Punctuation 28210
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1530370
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 28210
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1530370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.3%
. 500720
32.7%
1 47560
 
3.1%
- 28210
 
1.8%

hg_product_130
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47423 
1.0
 
137

Length

Max length4
Median length3
Mean length3.0947096
Min length3

Characters and Unicode

Total characters1549583
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47423
 
9.5%
1.0 137
 
< 0.1%

Length

2023-12-31T13:48:52.642967image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:52.824922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47423
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47423
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549583
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47423
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47423
 
3.1%

hg_product_131
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47348 
1.0
 
212

Length

Max length4
Median length3
Mean length3.0945598
Min length3

Characters and Unicode

Total characters1549508
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47348
 
9.5%
1.0 212
 
< 0.1%

Length

2023-12-31T13:48:53.000332image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:53.157552image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47348
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47348
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47348
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47348
 
3.1%

hg_product_132
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
42447
1.0
 
5113

Length

Max length4
Median length3
Mean length3.0847719
Min length3

Characters and Unicode

Total characters1544607
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 42447
 
8.5%
1.0 5113
 
1.0%

Length

2023-12-31T13:48:53.333213image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:53.497800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42447
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 42447
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1544607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42447
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 42447
 
2.7%

hg_product_133
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47378 
1.0
 
182

Length

Max length4
Median length3
Mean length3.0946197
Min length3

Characters and Unicode

Total characters1549538
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47378
 
9.5%
1.0 182
 
< 0.1%

Length

2023-12-31T13:48:53.673965image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:53.832116image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47378
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47378
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47378
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47378
 
3.1%

hg_product_134
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47443 
1.0
 
117

Length

Max length4
Median length3
Mean length3.0947496
Min length3

Characters and Unicode

Total characters1549603
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47443
 
9.5%
1.0 117
 
< 0.1%

Length

2023-12-31T13:48:54.006156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:54.166575image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47443
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47443
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47443
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47443
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47443
 
3.1%

hg_product_135
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47548 
1.0
 
12

Length

Max length4
Median length3
Mean length3.0949593
Min length3

Characters and Unicode

Total characters1549708
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47548
 
9.5%
1.0 12
 
< 0.1%

Length

2023-12-31T13:48:54.339653image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:54.497797image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47548
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47548
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47548
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47548
 
3.1%

hg_product_136
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47538 
1.0
 
22

Length

Max length4
Median length3
Mean length3.0949393
Min length3

Characters and Unicode

Total characters1549698
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47538
 
9.5%
1.0 22
 
< 0.1%

Length

2023-12-31T13:48:54.672643image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:54.829840image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47538
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47538
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549698
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47538
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47538
 
3.1%

hg_product_137
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47130 
1.0
 
430

Length

Max length4
Median length3
Mean length3.0941245
Min length3

Characters and Unicode

Total characters1549290
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47130
 
9.4%
1.0 430
 
0.1%

Length

2023-12-31T13:48:55.004931image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:55.162911image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47130
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47130
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47130
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47130
 
3.0%

hg_product_138
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47332 
1.0
 
228

Length

Max length4
Median length3
Mean length3.0945279
Min length3

Characters and Unicode

Total characters1549492
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47332
 
9.5%
1.0 228
 
< 0.1%

Length

2023-12-31T13:48:55.337124image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:55.506039image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47332
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47332
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47332
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47332
 
3.1%

hg_product_139
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
44230
1.0
 
3330

Length

Max length4
Median length3
Mean length3.0883328
Min length3

Characters and Unicode

Total characters1546390
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 44230
 
8.8%
1.0 3330
 
0.7%

Length

2023-12-31T13:48:55.682540image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:56.075808image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44230
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 44230
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1546390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44230
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1546390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 44230
 
2.9%

hg_product_140
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
40816
1.0
 
6744

Length

Max length4
Median length3
Mean length3.0815146
Min length3

Characters and Unicode

Total characters1542976
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 40816
 
8.2%
1.0 6744
 
1.3%

Length

2023-12-31T13:48:56.246208image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:56.402354image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.5%
1 47560
 
3.1%
- 40816
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.5%
Dash Punctuation 40816
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40816
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1542976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.5%
1 47560
 
3.1%
- 40816
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.5%
1 47560
 
3.1%
- 40816
 
2.6%

hg_product_141
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
46689 
1.0
 
871

Length

Max length4
Median length3
Mean length3.0932437
Min length3

Characters and Unicode

Total characters1548849
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 46689
 
9.3%
1.0 871
 
0.2%

Length

2023-12-31T13:48:56.574589image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:56.739152image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46689
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.7%
Other Punctuation 500720
32.3%
Dash Punctuation 46689
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1548849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46689
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 46689
 
3.0%

hg_product_142
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47156 
1.0
 
404

Length

Max length4
Median length3
Mean length3.0941764
Min length3

Characters and Unicode

Total characters1549316
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47156
 
9.4%
1.0 404
 
0.1%

Length

2023-12-31T13:48:56.915290image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:57.076547image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47156
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47156
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549316
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47156
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47156
 
3.0%

hg_product_143
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
43476
1.0
 
4084

Length

Max length4
Median length3
Mean length3.086827
Min length3

Characters and Unicode

Total characters1545636
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 43476
 
8.7%
1.0 4084
 
0.8%

Length

2023-12-31T13:48:57.251911image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:57.412403image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43476
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.8%
Other Punctuation 500720
32.4%
Dash Punctuation 43476
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1545636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43476
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.7%
. 500720
32.4%
1 47560
 
3.1%
- 43476
 
2.8%

hg_product_144
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
33878
1.0
 
13682

Length

Max length4
Median length3
Mean length3.0676586
Min length3

Characters and Unicode

Total characters1536038
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 33878
 
6.8%
1.0 13682
 
2.7%

Length

2023-12-31T13:48:57.584443image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:57.745728image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33878
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
65.2%
Other Punctuation 500720
32.6%
Dash Punctuation 33878
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33878
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1536038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33878
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1536038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
62.1%
. 500720
32.6%
1 47560
 
3.1%
- 33878
 
2.2%

hg_product_145
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
 
41520
1.0
 
6040

Length

Max length4
Median length3
Mean length3.0829206
Min length3

Characters and Unicode

Total characters1543680
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 41520
 
8.3%
1.0 6040
 
1.2%

Length

2023-12-31T13:48:57.914575image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:58.074802image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41520
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.9%
Other Punctuation 500720
32.4%
Dash Punctuation 41520
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1543680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41520
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1543680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.8%
. 500720
32.4%
1 47560
 
3.1%
- 41520
 
2.7%

hg_product_146
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47390 
1.0
 
170

Length

Max length4
Median length3
Mean length3.0946437
Min length3

Characters and Unicode

Total characters1549550
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47390
 
9.5%
1.0 170
 
< 0.1%

Length

2023-12-31T13:48:58.248918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:58.427129image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47390
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47390
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47390
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47390
 
3.1%

hg_product_147
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47241 
1.0
 
319

Length

Max length4
Median length3
Mean length3.0943461
Min length3

Characters and Unicode

Total characters1549401
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47241
 
9.4%
1.0 319
 
0.1%

Length

2023-12-31T13:48:58.646891image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:58.834907image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47241
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47241
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549401
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47241
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47241
 
3.0%

hg_product_148
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
0.0
453160 
-1.0
47520 
1.0
 
40

Length

Max length4
Median length3
Mean length3.0949033
Min length3

Characters and Unicode

Total characters1549680
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453160
90.5%
-1.0 47520
 
9.5%
1.0 40
 
< 0.1%

Length

2023-12-31T13:48:59.019447image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:48:59.182765image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453160
90.5%
1.0 47560
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47520
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001440
64.6%
Other Punctuation 500720
32.3%
Dash Punctuation 47520
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 953880
95.3%
1 47560
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 500720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47520
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 953880
61.6%
. 500720
32.3%
1 47560
 
3.1%
- 47520
 
3.1%

country_top
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 MiB
US
187691 
Other
113568 
GB
43663 
Missing
39646 
IN
32637 
Other values (6)
83515 

Length

Max length7
Median length2
Mean length3.0763181
Min length2

Characters and Unicode

Total characters1540374
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowNL
3rd rowMissing
4th rowOther
5th rowIN

Common Values

ValueCountFrequency (%)
US 187691
37.5%
Other 113568
22.7%
GB 43663
 
8.7%
Missing 39646
 
7.9%
IN 32637
 
6.5%
DE 19998
 
4.0%
CA 16043
 
3.2%
FR 15916
 
3.2%
AU 15048
 
3.0%
NL 9102
 
1.8%

Length

2023-12-31T13:48:59.372311image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 187691
37.5%
other 113568
22.7%
gb 43663
 
8.7%
missing 39646
 
7.9%
in 32637
 
6.5%
de 19998
 
4.0%
ca 16043
 
3.2%
fr 15916
 
3.2%
au 15048
 
3.0%
nl 9102
 
1.8%

Most occurring characters

ValueCountFrequency (%)
U 202739
13.2%
S 187691
12.2%
O 113568
 
7.4%
t 113568
 
7.4%
h 113568
 
7.4%
e 113568
 
7.4%
r 113568
 
7.4%
i 79292
 
5.1%
s 79292
 
5.1%
B 51071
 
3.3%
Other values (13) 372449
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 848226
55.1%
Lowercase Letter 692148
44.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 202739
23.9%
S 187691
22.1%
O 113568
13.4%
B 51071
 
6.0%
G 43663
 
5.1%
N 41739
 
4.9%
M 39646
 
4.7%
I 32637
 
3.8%
A 31091
 
3.7%
R 23324
 
2.7%
Other values (5) 81057
 
9.6%
Lowercase Letter
ValueCountFrequency (%)
t 113568
16.4%
h 113568
16.4%
e 113568
16.4%
r 113568
16.4%
i 79292
11.5%
s 79292
11.5%
n 39646
 
5.7%
g 39646
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1540374
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 202739
13.2%
S 187691
12.2%
O 113568
 
7.4%
t 113568
 
7.4%
h 113568
 
7.4%
e 113568
 
7.4%
r 113568
 
7.4%
i 79292
 
5.1%
s 79292
 
5.1%
B 51071
 
3.3%
Other values (13) 372449
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1540374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 202739
13.2%
S 187691
12.2%
O 113568
 
7.4%
t 113568
 
7.4%
h 113568
 
7.4%
e 113568
 
7.4%
r 113568
 
7.4%
i 79292
 
5.1%
s 79292
 
5.1%
B 51071
 
3.3%
Other values (13) 372449
24.2%

state_top
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.7 MiB
Missing
315976 
Other
69722 
CA
40081 
NY
 
20048
TX
 
13005
Other values (6)
41888 

Length

Max length7
Median length7
Mean length5.572947
Min length2

Characters and Unicode

Total characters2790486
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing

Common Values

ValueCountFrequency (%)
Missing 315976
63.1%
Other 69722
 
13.9%
CA 40081
 
8.0%
NY 20048
 
4.0%
TX 13005
 
2.6%
FL 10143
 
2.0%
IL 7708
 
1.5%
MA 7049
 
1.4%
WA 5989
 
1.2%
CO 5776
 
1.2%

Length

2023-12-31T13:48:59.572272image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing 315976
63.1%
other 69722
 
13.9%
ca 40081
 
8.0%
ny 20048
 
4.0%
tx 13005
 
2.6%
fl 10143
 
2.0%
il 7708
 
1.5%
ma 7049
 
1.4%
wa 5989
 
1.2%
co 5776
 
1.2%

Most occurring characters

ValueCountFrequency (%)
s 631952
22.6%
i 631952
22.6%
M 323025
11.6%
n 315976
11.3%
g 315976
11.3%
O 75498
 
2.7%
t 69722
 
2.5%
h 69722
 
2.5%
e 69722
 
2.5%
r 69722
 
2.5%
Other values (11) 217219
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2174744
77.9%
Uppercase Letter 615742
 
22.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 323025
52.5%
O 75498
 
12.3%
A 58342
 
9.5%
C 45857
 
7.4%
N 20048
 
3.3%
Y 20048
 
3.3%
L 17851
 
2.9%
T 13005
 
2.1%
X 13005
 
2.1%
F 10143
 
1.6%
Other values (3) 18920
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
s 631952
29.1%
i 631952
29.1%
n 315976
14.5%
g 315976
14.5%
t 69722
 
3.2%
h 69722
 
3.2%
e 69722
 
3.2%
r 69722
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2790486
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 631952
22.6%
i 631952
22.6%
M 323025
11.6%
n 315976
11.3%
g 315976
11.3%
O 75498
 
2.7%
t 69722
 
2.5%
h 69722
 
2.5%
e 69722
 
2.5%
r 69722
 
2.5%
Other values (11) 217219
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2790486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 631952
22.6%
i 631952
22.6%
M 323025
11.6%
n 315976
11.3%
g 315976
11.3%
O 75498
 
2.7%
t 69722
 
2.5%
h 69722
 
2.5%
e 69722
 
2.5%
r 69722
 
2.5%
Other values (11) 217219
 
7.8%

country_top_AU
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
485672 
True
 
15048
ValueCountFrequency (%)
False 485672
97.0%
True 15048
 
3.0%
2023-12-31T13:48:59.729044image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_BR
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
493312 
True
 
7408
ValueCountFrequency (%)
False 493312
98.5%
True 7408
 
1.5%
2023-12-31T13:48:59.863013image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_CA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
484677 
True
 
16043
ValueCountFrequency (%)
False 484677
96.8%
True 16043
 
3.2%
2023-12-31T13:48:59.994420image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_DE
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
480722 
True
 
19998
ValueCountFrequency (%)
False 480722
96.0%
True 19998
 
4.0%
2023-12-31T13:49:00.125764image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_FR
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
484804 
True
 
15916
ValueCountFrequency (%)
False 484804
96.8%
True 15916
 
3.2%
2023-12-31T13:49:00.259418image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_GB
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
457057 
True
 
43663
ValueCountFrequency (%)
False 457057
91.3%
True 43663
 
8.7%
2023-12-31T13:49:00.392998image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_IN
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
468083 
True
 
32637
ValueCountFrequency (%)
False 468083
93.5%
True 32637
 
6.5%
2023-12-31T13:49:00.531575image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_Missing
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
461074 
True
 
39646
ValueCountFrequency (%)
False 461074
92.1%
True 39646
 
7.9%
2023-12-31T13:49:00.663832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_NL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
491618 
True
 
9102
ValueCountFrequency (%)
False 491618
98.2%
True 9102
 
1.8%
2023-12-31T13:49:00.794887image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
387152 
True
113568 
ValueCountFrequency (%)
False 387152
77.3%
True 113568
 
22.7%
2023-12-31T13:49:00.929808image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_US
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
313029 
True
187691 
ValueCountFrequency (%)
False 313029
62.5%
True 187691
37.5%
2023-12-31T13:49:01.065752image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_CA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
460639 
True
 
40081
ValueCountFrequency (%)
False 460639
92.0%
True 40081
 
8.0%
2023-12-31T13:49:01.201804image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_CO
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
494944 
True
 
5776
ValueCountFrequency (%)
False 494944
98.8%
True 5776
 
1.2%
2023-12-31T13:49:01.339788image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_FL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
490577 
True
 
10143
ValueCountFrequency (%)
False 490577
98.0%
True 10143
 
2.0%
2023-12-31T13:49:01.471096image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_GA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
495497 
True
 
5223
ValueCountFrequency (%)
False 495497
99.0%
True 5223
 
1.0%
2023-12-31T13:49:01.603421image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_IL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
493012 
True
 
7708
ValueCountFrequency (%)
False 493012
98.5%
True 7708
 
1.5%
2023-12-31T13:49:01.741723image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_MA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
493671 
True
 
7049
ValueCountFrequency (%)
False 493671
98.6%
True 7049
 
1.4%
2023-12-31T13:49:01.875627image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_Missing
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
True
315976 
False
184744 
ValueCountFrequency (%)
True 315976
63.1%
False 184744
36.9%
2023-12-31T13:49:02.011211image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_NY
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
480672 
True
 
20048
ValueCountFrequency (%)
False 480672
96.0%
True 20048
 
4.0%
2023-12-31T13:49:02.153120image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
430998 
True
69722 
ValueCountFrequency (%)
False 430998
86.1%
True 69722
 
13.9%
2023-12-31T13:49:02.288577image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_TX
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
487715 
True
 
13005
ValueCountFrequency (%)
False 487715
97.4%
True 13005
 
2.6%
2023-12-31T13:49:02.494336image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_WA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
494731 
True
 
5989
ValueCountFrequency (%)
False 494731
98.8%
True 5989
 
1.2%
2023-12-31T13:49:02.627247image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
442142 
True
58578 
ValueCountFrequency (%)
False 442142
88.3%
True 58578
 
11.7%
2023-12-31T13:49:02.762194image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_2. Tech - Software Publisher
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
494447 
True
 
6273
ValueCountFrequency (%)
False 494447
98.7%
True 6273
 
1.3%
2023-12-31T13:49:02.902832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_3. Finance
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
473415 
True
 
27305
ValueCountFrequency (%)
False 473415
94.5%
True 27305
 
5.5%
2023-12-31T13:49:03.035265image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_4. Consulting
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
481771 
True
 
18949
ValueCountFrequency (%)
False 481771
96.2%
True 18949
 
3.8%
2023-12-31T13:49:03.168563image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_5. Retail
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
477506 
True
 
23214
ValueCountFrequency (%)
False 477506
95.4%
True 23214
 
4.6%
2023-12-31T13:49:03.305257image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_6. Manufacturing
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
462241 
True
 
38479
ValueCountFrequency (%)
False 462241
92.3%
True 38479
 
7.7%
2023-12-31T13:49:03.437678image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_7. Wholesale Trade
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
480719 
True
 
20001
ValueCountFrequency (%)
False 480719
96.0%
True 20001
 
4.0%
2023-12-31T13:49:03.569440image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_8. Healthcare
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
485875 
True
 
14845
ValueCountFrequency (%)
False 485875
97.0%
True 14845
 
3.0%
2023-12-31T13:49:03.706313image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_9. Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
276439 
True
224281 
ValueCountFrequency (%)
False 276439
55.2%
True 224281
44.8%
2023-12-31T13:49:03.854121image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_nan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
431925 
True
68795 
ValueCountFrequency (%)
False 431925
86.3%
True 68795
 
13.7%
2023-12-31T13:49:03.991099image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Over 10B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
498314 
True
 
2406
ValueCountFrequency (%)
False 498314
99.5%
True 2406
 
0.5%
2023-12-31T13:49:04.123528image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 100M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
491581 
True
 
9139
ValueCountFrequency (%)
False 491581
98.2%
True 9139
 
1.8%
2023-12-31T13:49:04.248948image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 10B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
492771 
True
 
7949
ValueCountFrequency (%)
False 492771
98.4%
True 7949
 
1.6%
2023-12-31T13:49:04.377742image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 10M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
499064 
True
 
1656
ValueCountFrequency (%)
False 499064
99.7%
True 1656
 
0.3%
2023-12-31T13:49:04.505484image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 1B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
482401 
True
 
18319
ValueCountFrequency (%)
False 482401
96.3%
True 18319
 
3.7%
2023-12-31T13:49:04.632479image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 1M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
499958 
True
 
762
ValueCountFrequency (%)
False 499958
99.8%
True 762
 
0.2%
2023-12-31T13:49:04.763512image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_nan
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
True
460489 
False
 
40231
ValueCountFrequency (%)
True 460489
92.0%
False 40231
 
8.0%
2023-12-31T13:49:04.895243image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_After 2000
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
365520 
True
135200 
ValueCountFrequency (%)
False 365520
73.0%
True 135200
 
27.0%
2023-12-31T13:49:05.046055image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_Before 2000
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
False
431417 
True
69303 
ValueCountFrequency (%)
False 431417
86.2%
True 69303
 
13.8%
2023-12-31T13:49:05.185233image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_nan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
True
296217 
False
204503 
ValueCountFrequency (%)
True 296217
59.2%
False 204503
40.8%
2023-12-31T13:49:05.335356image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2023-12-31T13:49:05.847905image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
company_revenue_bucketcountry_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_UScrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product1_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customerdnb_founded_time_groupedfounded_After 2000founded_Before 2000founded_nanhas_crossbeam_datahas_hg_datahg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148hg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99industry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_groupedindustry_nanrevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanstatestate_topstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WA
company_revenue_bucket1.0000.0540.0230.0230.0000.0350.0290.0600.0680.0100.0270.0430.0490.0950.0950.1030.0960.1110.0970.0950.1050.0980.1330.1010.1270.1080.1170.0980.0950.0950.0950.1320.1390.1200.0990.1050.1040.0630.0800.0470.1290.2600.1940.1880.2580.2260.1790.2170.1630.1640.1560.1580.1630.1560.2160.1560.2080.1600.1690.1610.1570.2210.1670.1580.1560.1580.2490.2300.1820.1750.1560.2150.1610.1640.2540.1690.1600.1570.1560.1730.1630.2090.2240.1820.1650.2380.2550.1980.1580.1600.1550.1550.2020.1750.1830.2210.2150.2550.2470.1650.2190.2160.2330.2370.1620.2310.2210.1720.2210.2400.1620.2130.2220.1900.2220.2030.1730.1810.1560.1950.1570.1800.1550.1550.1580.1550.2190.2460.1610.2230.1620.2300.2060.1950.1790.1620.1880.2300.1940.2030.2180.2210.1930.2120.1920.2080.1550.2340.1760.1570.1700.2260.1820.1640.1660.1560.1750.1560.1590.2110.2280.1640.1750.1730.1200.0440.0650.0470.0360.0810.0170.0550.0560.0820.0541.0001.0001.0001.0001.0001.0001.0000.0480.0310.0320.0000.0050.0130.0150.0000.0480.0170.0350.0220.030
country_top0.0541.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.1720.1710.1730.1710.1720.1720.1710.1710.1720.1720.1710.1720.1720.1710.1740.1710.1710.1710.1720.1740.1730.1710.1710.2460.3730.1570.4080.2420.1150.0830.0830.0880.0860.0820.0850.0820.0820.0810.0830.0820.0810.0840.0810.0840.0820.0820.0820.0810.0840.0820.0810.0810.0870.0890.0890.0830.0820.0810.1060.0830.0820.0870.0810.0820.0810.0820.0830.0830.0900.0950.0830.0830.0890.0880.0930.0820.0820.0810.0810.0880.0830.0830.0930.0910.0870.0850.0820.0880.0870.0890.0870.0820.0850.0890.0830.0870.0870.0810.0880.0900.0820.0850.0840.0840.0820.0810.0960.0820.0890.0810.0810.0820.0810.0850.0870.0810.0850.0840.0920.0860.0960.0840.0820.0870.0980.0860.0880.0900.0900.0850.0880.0840.0870.0810.0890.0830.0810.0820.0910.0960.0850.0860.0810.0820.0810.0820.0830.0860.0820.0820.0820.1530.0810.0690.1000.0710.0830.0660.0820.1940.0750.5830.0270.0460.0480.0220.0720.0200.1031.0000.3120.3810.1390.1860.1330.1610.1540.9870.2640.5190.2110.142
country_top_AU0.0231.0001.0000.0210.0320.0360.0320.0540.0460.0520.0240.0950.1360.0020.0000.0060.0010.0050.0010.0000.0010.0040.0000.0040.0070.0000.0020.0090.0000.0000.0000.0030.0080.0000.0060.0020.0200.0340.0030.0330.0000.0020.0060.0010.0050.0050.0020.0010.0010.0020.0010.0020.0010.0020.0020.0010.0020.0010.0030.0030.0020.0050.0020.0020.0010.0060.0080.0050.0030.0010.0020.0090.0030.0020.0030.0010.0020.0010.0020.0050.0010.0030.0030.0010.0040.0010.0060.0030.0010.0030.0030.0010.0020.0040.0010.0010.0030.0080.0070.0020.0040.0030.0110.0090.0020.0060.0070.0020.0100.0070.0020.0020.0020.0010.0010.0030.0060.0040.0020.0070.0020.0070.0020.0010.0020.0020.0020.0030.0020.0060.0040.0120.0070.0050.0040.0010.0060.0090.0050.0060.0050.0010.0040.0010.0010.0010.0010.0100.0030.0010.0040.0110.0040.0010.0010.0010.0030.0010.0030.0040.0020.0010.0040.0030.0240.0110.0200.0060.0020.0000.0040.0070.0230.0380.0190.0030.0080.0020.0030.0000.0030.0081.0000.1350.0520.0190.0250.0180.0220.0210.1350.0360.0710.0290.019
country_top_BR0.0231.0000.0211.0000.0220.0250.0220.0380.0320.0360.0170.0660.0950.0110.0110.0110.0110.0120.0110.0110.0110.0110.0120.0100.0130.0110.0110.0190.0100.0110.0110.0110.0210.0110.0110.0100.0090.0550.0320.0720.0110.0150.0160.0160.0210.0210.0160.0150.0150.0190.0160.0150.0150.0150.0200.0150.0150.0150.0160.0160.0160.0180.0160.0150.0150.0170.0260.0290.0160.0150.0150.0160.0160.0150.0180.0150.0160.0150.0150.0160.0160.0190.0210.0150.0150.0160.0180.0210.0150.0160.0150.0150.0150.0150.0150.0150.0210.0150.0180.0150.0160.0150.0150.0160.0150.0160.0160.0160.0150.0170.0160.0230.0180.0150.0170.0160.0160.0160.0150.0160.0150.0190.0150.0150.0150.0150.0150.0150.0150.0150.0160.0160.0180.0220.0170.0160.0150.0230.0200.0160.0180.0160.0150.0150.0150.0150.0150.0240.0170.0150.0150.0280.0200.0160.0170.0150.0150.0150.0160.0170.0160.0150.0150.0160.0340.0310.0090.0180.0030.0140.0150.0030.0220.0610.0180.0000.0030.0030.0030.0000.0030.0001.0000.0940.0360.0130.0180.0120.0150.0150.0940.0250.0490.0200.013
country_top_CA0.0001.0000.0320.0221.0000.0370.0330.0560.0480.0530.0250.0990.1410.0050.0040.0060.0040.0050.0050.0040.0040.0040.0090.0040.0040.0040.0040.0050.0040.0040.0040.0050.0050.0050.0040.0040.0210.0200.0060.0130.0040.0000.0040.0000.0090.0050.0060.0090.0080.0000.0000.0000.0010.0000.0000.0000.0000.0000.0040.0030.0000.0030.0020.0000.0000.0020.0080.0030.0000.0000.0030.0110.0000.0030.0000.0000.0000.0000.0010.0000.0000.0040.0070.0000.0000.0000.0100.0020.0000.0000.0010.0000.0000.0000.0000.0030.0050.0000.0000.0000.0030.0150.0150.0130.0000.0120.0100.0070.0150.0100.0010.0110.0100.0000.0040.0030.0000.0020.0030.0020.0000.0000.0020.0000.0000.0000.0070.0050.0000.0020.0000.0130.0020.0000.0000.0000.0060.0210.0020.0050.0090.0080.0030.0120.0060.0120.0020.0050.0000.0000.0030.0070.0000.0030.0000.0000.0020.0000.0130.0020.0030.0000.0040.0000.0140.0230.0080.0080.0070.0040.0040.0030.0330.0380.0300.0000.0020.0000.0010.0000.0000.0001.0000.1390.0540.0200.0260.0190.0230.0220.1390.0370.0730.0300.020
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hg_product_980.1750.0820.0040.0150.0040.0270.0330.0050.0000.0940.0140.0580.0280.1890.1890.1910.1890.1930.1900.1890.1910.1890.1990.1910.1970.1910.1950.1910.1890.1890.1890.2040.2030.1950.1910.1940.2000.0210.1830.1100.2650.9860.7340.7230.7180.7280.7240.7330.7130.7180.7070.7070.7090.7100.7300.7070.7390.7190.7240.7130.7090.7320.7090.7110.7080.7110.7170.7130.7220.7200.7070.7110.7070.7080.7140.7110.7110.7100.7080.7210.7120.7230.7190.7220.7170.7240.7140.7130.7080.7070.7070.7070.7220.7200.7190.7180.7110.7180.7170.7180.7210.7250.7200.7220.7110.7240.7250.7240.7220.7200.7080.7110.7090.7110.7190.7250.7090.7210.7070.7130.7080.7120.7070.7070.7070.7070.7290.7210.7110.7280.7090.7120.7170.7160.7160.7120.7130.7110.7170.7190.7190.7200.7260.7270.7290.7260.7070.7150.7210.7080.7120.7120.7140.7140.7090.7070.7300.7070.7180.7350.7290.7101.0000.7210.0620.0090.0600.0340.0100.0720.0170.0560.0370.0890.1050.1780.3980.3370.1620.5720.1160.8340.0560.0320.0320.0140.0100.0020.0070.0000.0270.0160.0140.0000.009
hg_product_990.1730.0820.0030.0160.0000.0270.0330.0040.0000.0940.0150.0580.0280.1900.1890.1910.1890.1930.1900.1890.1900.1890.1970.1900.1960.1900.1920.1910.1890.1890.1890.1980.2020.1960.1910.1910.2000.0210.1830.1100.2650.9860.7220.7190.7170.7220.7180.7250.7090.7170.7080.7080.7080.7100.7230.7070.7300.7110.7130.7150.7080.7270.7100.7070.7070.7100.7160.7130.7160.7110.7070.7110.7070.7080.7140.7100.7070.7070.7080.7170.7090.7170.7150.7170.7090.7200.7140.7110.7080.7070.7070.7070.7170.7160.7210.7160.7110.7160.7160.7080.7160.7180.7140.7150.7160.7160.7180.7160.7170.7160.7070.7110.7090.7120.7150.7190.7100.7220.7080.7110.7070.7110.7070.7070.7070.7070.7190.7170.7080.7210.7100.7110.7140.7160.7120.7090.7120.7100.7130.7160.7170.7170.7230.7180.7160.7160.7070.7140.7140.7070.7110.7120.7120.7110.7100.7070.7130.7070.7080.7220.7230.7090.7211.0000.0620.0090.0540.0340.0100.0710.0170.0560.0370.0880.1050.1730.3980.3370.1620.5720.1160.8340.0550.0320.0330.0140.0110.0020.0080.0020.0270.0140.0110.0060.008
industry_1. Tech - Computer systems design and related services0.1200.1530.0240.0340.0140.0270.0420.0060.0880.1070.0020.0380.0260.0650.0590.0600.0590.0590.0640.0590.0590.0590.0610.0590.0600.0590.0590.0640.0590.0590.0590.0610.0590.0610.0590.0590.2050.2650.0410.2100.0590.0610.0620.0620.0620.0630.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0630.0620.0620.0620.0630.0620.0640.0620.0620.0620.0630.0620.0620.0620.0620.0620.0620.0620.0630.0620.0630.0630.0620.0620.0630.0630.0640.0630.0630.0620.0620.0640.0630.0620.0630.0630.0630.0650.0620.0630.0630.0620.0620.0620.0620.0620.0620.0620.0630.0620.0620.0630.0620.0630.0630.0620.0630.0620.0640.0620.0630.0620.0620.0620.0620.0620.0630.0620.0620.0620.0640.0630.0640.0620.0620.0620.0630.0630.0630.0630.0630.0630.0620.0620.0620.0620.0640.0630.0620.0630.0630.0630.0630.0630.0620.0620.0620.0620.0620.0620.0620.0620.0621.0000.0410.0870.0720.0800.1050.0740.0640.3281.0000.1450.0170.0130.0360.0000.0490.0060.0610.0740.0360.0100.0040.0120.0030.0070.0000.0240.0220.0190.0030.000
industry_2. Tech - Software Publisher0.0440.0810.0110.0310.0230.0090.0430.0160.0210.0330.0140.0220.0370.0670.0540.0540.0540.0540.0580.0540.0540.0540.0540.0540.0540.0540.0540.0610.0540.0540.0540.0540.0540.0540.0540.0540.0410.0470.0100.0360.0540.0090.0100.0100.0110.0100.0100.0100.0090.0090.0090.0090.0090.0090.0100.0090.0110.0090.0110.0100.0090.0100.0090.0090.0090.0190.0100.0220.0090.0090.0100.0170.0090.0090.0090.0090.0090.0090.0090.0130.0100.0150.0190.0090.0120.0130.0170.0190.0120.0120.0100.0090.0170.0120.0120.0200.0190.0180.0200.0100.0150.0130.0120.0110.0100.0100.0120.0100.0110.0150.0090.0130.0120.0090.0150.0120.0090.0110.0090.0190.0090.0160.0090.0090.0100.0100.0090.0100.0090.0100.0130.0180.0120.0270.0120.0120.0140.0110.0090.0100.0100.0100.0110.0110.0100.0110.0090.0190.0110.0090.0100.0160.0220.0160.0170.0090.0090.0090.0090.0090.0090.0090.0090.0090.0411.0000.0270.0220.0250.0320.0230.0200.1011.0000.0450.0040.0000.0030.0090.0070.0000.0050.0380.0470.0350.0050.0000.0070.0020.0150.0380.0030.0050.0110.009
industry_3. Finance0.0650.0690.0200.0090.0080.0160.0030.0020.0170.0580.0000.0170.0260.0810.0800.0810.0800.0800.0800.0800.0800.0800.0810.0800.0820.0800.0800.0840.0800.0800.0800.0840.0810.0810.0800.0810.0440.0200.0240.0000.0800.0520.0550.0540.0650.0600.0540.0600.0530.0540.0530.0530.0530.0530.0620.0530.0570.0540.0540.0530.0530.0600.0530.0530.0530.0530.0680.0580.0570.0600.0530.0560.0530.0530.0530.0530.0530.0530.0530.0530.0530.0560.0590.0530.0530.0630.0690.0530.0530.0530.0530.0530.0570.0540.0540.0560.0540.0620.0660.0530.0580.0560.0590.0580.0530.0570.0570.0540.0570.0600.0530.0540.0530.0530.0560.0550.0530.0550.0530.0530.0530.0530.0530.0530.0530.0530.0720.0740.0540.0670.0530.0680.0530.0540.0530.0530.0530.0630.0540.0540.0560.0590.0560.0620.0570.0590.0530.0600.0540.0530.0530.0550.0530.0530.0530.0530.0530.0530.0530.0570.0580.0530.0600.0540.0870.0271.0000.0480.0530.0690.0490.0420.2161.0000.0960.0230.0090.0370.0060.0380.0080.0560.0560.0390.0010.0040.0040.0000.0120.0090.0270.0290.0090.0070.005
industry_4. Consulting0.0470.1000.0060.0180.0080.0180.0210.0300.0170.0580.0120.0150.0840.0130.0130.0130.0130.0140.0140.0130.0130.0130.0140.0130.0130.0130.0130.0140.0130.0130.0130.0140.0130.0130.0130.0130.0720.0570.0280.0310.0130.0330.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0350.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0720.0220.0481.0000.0440.0570.0400.0350.1791.0000.0790.0070.0100.0190.0030.0270.0030.0350.0480.0880.0130.0160.0270.0180.0220.0150.0840.0130.0520.0220.006
industry_5. Retail0.0360.0710.0020.0030.0070.0110.0110.0410.0200.0540.0050.0020.0130.0280.0270.0270.0270.0270.0290.0270.0270.0270.0270.0270.0270.0270.0270.0360.0270.0270.0270.0270.0300.0270.0270.0270.0180.0100.0230.0250.0270.0110.0100.0110.0120.0110.0100.0110.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0110.0100.0100.0110.0110.0130.0100.0100.0100.0100.0100.0100.0100.0100.0110.0100.0100.0100.0100.0100.0110.0100.0100.0110.0100.0120.0100.0100.0100.0100.0110.0110.0100.0100.0100.0110.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0190.0120.0170.0170.0110.0100.0100.0100.0130.0100.0120.0100.0100.0110.0100.0100.0100.0100.0110.0100.0110.0110.0120.0100.0100.0170.0130.0110.0120.0110.0110.0100.0100.0100.0100.0100.0110.0100.0100.0100.0110.0100.0100.0100.0100.0100.0100.0100.0100.0110.0100.0100.0100.0800.0250.0530.0441.0000.0640.0450.0380.1991.0000.0880.0070.0000.0100.0020.0130.0000.0140.0350.0210.0080.0030.0030.0020.0020.0050.0150.0130.0090.0020.000
industry_6. Manufacturing0.0810.0830.0000.0140.0040.0300.0130.0110.0450.0600.0160.0020.0090.0280.0270.0260.0270.0270.0320.0270.0280.0270.0280.0270.0270.0270.0260.0280.0260.0270.0270.0270.0270.0270.0260.0260.1590.0270.1330.0690.0270.0720.0720.0710.0720.0720.0710.0720.0710.0720.0710.0710.0710.0710.0720.0710.0720.0710.0720.0710.0710.0730.0750.0710.0710.0720.0720.0740.0710.0710.0710.0720.0720.0720.0830.0710.0720.0710.0710.0710.0710.0710.0710.0710.0710.0720.0720.0720.0710.0710.0710.0710.0720.0710.0710.0720.0770.0720.0720.0710.0720.0710.0710.0710.0710.0710.0710.0710.0710.0710.0710.0720.0740.0720.0730.0720.0720.0720.0710.0720.0710.0710.0710.0710.0710.0710.0720.0720.0710.0720.0710.0720.0730.0710.0710.0710.0720.0720.0720.0730.0730.0720.0710.0720.0720.0720.0710.0720.0720.0710.0710.0740.0710.0710.0710.0710.0720.0710.0710.0720.0710.0710.0720.0710.1050.0320.0690.0570.0641.0000.0590.0500.2601.0000.1150.0270.0230.0550.0030.0660.0000.0890.0720.0270.0090.0040.0100.0020.0050.0100.0100.0100.0170.0080.000
industry_7. Wholesale Trade0.0170.0660.0040.0150.0040.0040.0080.0060.0270.0540.0050.0270.0000.0140.0130.0130.0130.0130.0170.0130.0130.0130.0130.0130.0140.0130.0130.0140.0130.0130.0130.0140.0140.0130.0130.0130.0780.0260.0980.0920.0130.0170.0160.0160.0190.0180.0160.0180.0160.0160.0160.0160.0160.0160.0170.0160.0170.0160.0160.0160.0160.0180.0170.0160.0160.0170.0200.0200.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0170.0170.0170.0160.0170.0180.0170.0160.0160.0160.0160.0170.0160.0170.0180.0220.0190.0190.0160.0180.0170.0160.0170.0160.0160.0160.0160.0160.0170.0160.0160.0170.0160.0160.0160.0160.0160.0160.0170.0160.0170.0160.0160.0160.0160.0180.0180.0160.0170.0160.0160.0180.0170.0160.0160.0160.0180.0180.0170.0180.0180.0170.0180.0160.0180.0160.0190.0160.0160.0160.0200.0160.0160.0160.0160.0160.0160.0160.0160.0170.0160.0170.0170.0740.0230.0490.0400.0450.0591.0000.0360.1841.0000.0810.0040.0050.0130.0020.0160.0000.0210.0340.0100.0040.0030.0000.0000.0050.0020.0020.0020.0070.0020.000
industry_8. Healthcare0.0550.0820.0070.0030.0030.0040.0010.0150.0210.0440.0040.0370.0720.0330.0330.0340.0330.0340.0330.0330.0330.0330.0330.0330.0330.0330.0330.0380.0330.0330.0330.0330.0330.0330.0330.0330.0570.0260.0310.0000.0330.0570.0560.0560.0580.0570.0560.0570.0560.0560.0560.0560.0560.0560.0560.0560.0570.0560.0560.0560.0560.0570.0560.0560.0560.0560.0580.0590.0560.0560.0560.0570.0560.0560.0610.0560.0560.0560.0560.0560.0560.0560.0570.0560.0560.0560.0580.0570.0560.0560.0560.0560.0570.0560.0570.0570.0600.0570.0590.0560.0560.0560.0560.0570.0560.0560.0560.0560.0560.0570.0560.0570.0560.0570.0570.0560.0560.0560.0560.0570.0560.0560.0560.0560.0560.0560.0560.0570.0560.0560.0560.0600.0570.0560.0560.0560.0570.0570.0560.0560.0560.0560.0560.0560.0560.0560.0560.0600.0560.0560.0560.0580.0560.0560.0560.0560.0560.0560.0560.0560.0560.0560.0560.0560.0640.0200.0420.0350.0380.0500.0361.0000.1571.0000.0700.0020.0260.0090.0030.0420.0040.0470.0490.0750.0110.0110.0150.0030.0130.0150.0720.0140.0520.0130.012
industry_9. Other0.0560.1940.0230.0220.0330.0000.0140.0350.0210.1850.0240.0220.0480.0170.0160.0150.0150.0160.0160.0160.0160.0150.0160.0150.0160.0160.0150.0170.0150.0160.0150.0150.0160.0150.0160.0150.0250.0180.0090.0100.0160.0380.0360.0360.0370.0370.0370.0370.0360.0370.0360.0360.0360.0360.0380.0370.0370.0360.0360.0360.0360.0370.0380.0370.0360.0370.0370.0370.0370.0370.0360.0410.0360.0360.0380.0360.0370.0360.0360.0370.0370.0380.0400.0370.0360.0400.0400.0370.0370.0360.0360.0360.0380.0370.0370.0380.0390.0380.0400.0360.0380.0390.0380.0390.0360.0380.0380.0370.0380.0390.0370.0370.0370.0360.0380.0370.0360.0370.0360.0370.0360.0370.0360.0370.0360.0360.0400.0420.0370.0390.0360.0420.0370.0390.0360.0360.0370.0370.0360.0360.0370.0370.0370.0380.0370.0370.0360.0380.0370.0360.0370.0360.0380.0360.0370.0360.0360.0360.0360.0370.0370.0360.0370.0370.3280.1010.2160.1790.1990.2600.1840.1571.0001.0000.3590.0000.0200.0010.0080.0120.0100.0220.0420.0490.0050.0140.0150.0060.0050.0000.0450.0200.0250.0130.009
industry_grouped0.0820.0750.0380.0610.0380.0490.0650.0600.1100.0730.0320.0670.1150.0880.0800.0810.0800.0800.0850.0800.0810.0800.0820.0800.0820.0800.0800.0890.0800.0800.0800.0830.0810.0820.0800.0810.2730.2560.1650.2100.1130.1220.0880.0870.0920.0900.0880.0900.0870.0870.0870.0870.0870.0870.0910.0870.0890.0870.0880.0870.0870.0900.0890.0870.0870.0890.0940.0930.0880.0890.0870.0900.0870.0870.0940.0870.0870.0870.0870.0880.0870.0890.0900.0870.0870.0910.0950.0890.0870.0870.0870.0870.0900.0880.0880.0900.0930.0920.0950.0870.0900.0890.0890.0890.0870.0890.0890.0870.0890.0900.0870.0890.0880.0880.0900.0890.0870.0880.0870.0890.0870.0880.0870.0870.0870.0870.0950.0960.0870.0920.0870.0950.0880.0900.0870.0870.0880.0910.0880.0890.0890.0900.0880.0910.0890.0900.0870.0930.0880.0870.0870.0900.0890.0880.0880.0870.0870.0870.0870.0890.0890.0870.0890.0881.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0400.0370.0760.0110.1000.0120.1300.0530.0470.0390.0220.0350.0190.0270.0280.1140.0450.0730.0280.017
industry_nan0.0540.5830.0190.0180.0300.0160.0440.0590.0520.5800.0150.0600.1710.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.1560.0000.2420.1600.3310.1560.1110.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1050.1450.0450.0960.0790.0880.1150.0810.0700.3591.0001.0000.0200.0510.0430.0210.0690.0130.1040.0710.1750.0520.0260.0310.0240.0340.0320.1740.0420.1030.0370.023
revenue_Over 10B1.0000.0270.0030.0000.0000.0030.0090.0070.0090.0200.0010.0120.0060.0630.0630.0720.0640.0860.0630.0640.0820.0630.1230.0720.0910.0740.0920.0630.0640.0630.0640.1280.1020.1070.0700.0810.0490.0120.0390.0170.0610.1380.2010.1930.2240.2230.1830.2290.1610.1600.1500.1540.1620.1510.2290.1500.2190.1580.1690.1560.1530.2290.1650.1530.1510.1550.2240.2090.1920.1780.1510.1880.1590.1640.2530.1710.1580.1530.1500.1790.1620.2150.2240.1880.1630.2400.2260.1910.1540.1540.1500.1500.2100.1760.1920.2210.1940.2400.2340.1660.2240.2290.2320.2460.1580.2450.2320.1760.2260.2480.1590.1910.1730.1950.2190.2150.1660.1870.1500.1890.1540.1770.1500.1500.1530.1500.2240.2360.1570.2340.1580.1970.2100.1980.1830.1600.1890.1840.1760.1830.1990.2100.1940.2120.1950.2110.1500.2190.1790.1520.1680.1990.1830.1630.1650.1500.1800.1500.1540.2220.2280.1590.1780.1730.0170.0040.0230.0070.0070.0270.0040.0020.0000.0400.0201.0000.0090.0090.0030.0130.0020.2350.0240.0110.0000.0010.0020.0020.0030.0000.0060.0000.0050.0070.006
revenue_Under 100M1.0000.0460.0080.0030.0020.0050.0170.0110.0070.0400.0020.0110.0030.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0950.0960.0950.0950.0950.0800.0040.0790.0520.0950.4030.3980.3990.4110.4020.3970.3990.3970.3980.3970.3970.3980.3970.3990.3970.3990.3970.3980.3980.3970.4000.3980.3980.3970.3970.4070.4050.3980.3980.3970.4020.3980.3980.4040.3980.3970.3970.3970.3980.3970.3990.4000.3980.3980.4020.4050.4000.3970.3980.3970.3970.3980.3980.3980.4000.4040.4040.4020.3970.3990.3990.4020.4010.3980.4010.4010.3980.4010.4000.3980.4070.4250.3990.4000.3990.3990.3980.3970.3990.3970.3980.3970.3970.3970.3970.4010.4040.3970.4000.3970.4060.3990.3980.3980.3970.3990.4130.4010.4030.4040.4030.3980.4000.3990.3990.3970.4030.3980.3970.3980.4090.3980.3970.3970.3970.3980.3970.3980.3990.4020.3980.3980.3980.0130.0000.0090.0100.0000.0230.0050.0260.0200.0370.0510.0091.0000.0170.0080.0260.0050.4610.0310.0140.0100.0050.0020.0020.0010.0000.0030.0000.0070.0000.004
revenue_Under 10B1.0000.0480.0020.0030.0000.0120.0050.0080.0150.0370.0000.0280.0030.1330.1330.1380.1340.1390.1330.1330.1360.1330.1500.1370.1520.1330.1340.1340.1340.1330.1330.1470.1670.1400.1360.1360.0980.0120.0950.0560.1310.3340.3370.3380.3650.3430.3370.3450.3360.3370.3360.3360.3360.3360.3420.3360.3420.3360.3370.3380.3360.3430.3370.3360.3360.3360.3610.3530.3380.3380.3360.3580.3360.3360.3630.3370.3360.3360.3360.3360.3370.3450.3540.3370.3360.3490.3720.3390.3360.3360.3360.3360.3400.3370.3370.3470.3480.3600.3600.3360.3460.3420.3580.3530.3370.3470.3460.3380.3510.3540.3370.3440.3460.3400.3490.3380.3360.3370.3360.3410.3360.3390.3360.3360.3360.3360.3460.3560.3360.3430.3370.3640.3420.3430.3390.3360.3390.3610.3360.3370.3400.3430.3380.3450.3400.3460.3360.3520.3370.3360.3360.3480.3390.3370.3370.3360.3370.3360.3370.3400.3480.3370.3370.3370.0360.0030.0370.0190.0100.0550.0130.0090.0010.0760.0430.0090.0171.0000.0070.0250.0050.4300.0370.0180.0090.0040.0010.0000.0070.0000.0030.0030.0130.0020.005
revenue_Under 10M1.0000.0220.0030.0030.0010.0000.0110.0080.0010.0170.0020.0060.0050.0420.0410.0410.0410.0420.0420.0420.0410.0420.0420.0420.0430.0410.0410.0420.0420.0410.0410.0420.0430.0420.0430.0410.0140.0070.0200.0200.0410.1640.1620.1620.1650.1640.1620.1630.1620.1620.1620.1620.1620.1620.1630.1620.1620.1620.1620.1620.1620.1630.1620.1620.1620.1620.1640.1620.1620.1620.1620.1630.1620.1630.1630.1620.1620.1620.1620.1630.1620.1620.1620.1620.1620.1630.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1630.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1630.1730.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1630.1620.1630.1620.1620.1620.1620.1620.1620.1620.1660.1640.1650.1640.1640.1630.1620.1630.1620.1620.1630.1630.1620.1620.1630.1620.1620.1620.1620.1620.1620.1620.1630.1630.1620.1620.1620.0000.0090.0060.0030.0020.0030.0020.0030.0080.0110.0210.0030.0080.0071.0000.0110.0010.1950.0100.0060.0000.0020.0020.0000.0000.0030.0040.0020.0030.0000.000
revenue_Under 1B1.0000.0720.0000.0000.0000.0270.0180.0000.0110.0570.0100.0340.0100.1730.1730.1730.1730.1730.1750.1730.1730.1730.1740.1730.1750.1730.1730.1760.1730.1730.1730.1730.1760.1730.1730.1730.1450.0170.1370.0810.1730.5780.5730.5720.5770.5760.5730.5730.5720.5720.5710.5710.5710.5710.5740.5710.5740.5710.5720.5720.5710.5750.5720.5720.5710.5710.5770.5770.5720.5720.5710.5740.5720.5710.5750.5720.5710.5710.5710.5720.5720.5730.5740.5720.5720.5760.5760.5760.5720.5720.5710.5710.5730.5720.5720.5750.5770.5780.5770.5720.5740.5740.5730.5740.5720.5740.5730.5720.5730.5750.5710.5780.5860.5720.5750.5730.5730.5720.5720.5750.5710.5730.5710.5710.5720.5710.5740.5760.5720.5740.5720.5740.5740.5730.5720.5720.5730.5740.5780.5790.5790.5770.5740.5740.5730.5730.5710.5780.5720.5710.5720.5800.5730.5720.5720.5710.5720.5710.5720.5730.5750.5720.5720.5720.0490.0070.0380.0270.0130.0660.0160.0420.0120.1000.0690.0130.0260.0250.0111.0000.0070.6590.0570.0280.0210.0080.0070.0020.0050.0020.0100.0100.0120.0010.004
revenue_Under 1M1.0000.0200.0030.0030.0000.0060.0080.0040.0020.0110.0070.0070.0080.0290.0290.0290.0290.0290.0290.0290.0290.0290.0290.0290.0320.0290.0300.0300.0290.0290.0290.0310.0300.0300.0290.0290.0020.0060.0070.0110.0290.1170.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1170.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1190.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1170.1170.1170.1160.1160.1170.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.1160.0060.0000.0080.0030.0000.0000.0000.0040.0100.0120.0130.0020.0050.0050.0010.0071.0000.1320.0000.0070.0030.0010.0020.0010.0000.0000.0080.0000.0040.0000.002
revenue_nan1.0000.1030.0080.0000.0000.0290.0300.0000.0140.0860.0090.0470.0080.2560.2560.2590.2560.2610.2580.2560.2580.2560.2680.2580.2670.2560.2570.2590.2560.2560.2560.2660.2750.2620.2580.2570.2040.0190.1930.1180.2550.8370.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8350.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8350.8340.8340.8340.8340.8340.8340.8340.8340.8350.8430.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8370.8370.8350.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8350.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.0610.0050.0560.0350.0140.0890.0210.0470.0220.1300.1040.2350.4610.4300.1950.6590.1321.0000.0730.0350.0240.0120.0080.0000.0080.0030.0070.0090.0170.0030.006
state0.0481.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0670.0660.0680.0660.0660.0740.0660.0680.0660.0690.0670.0680.0670.0660.0720.0660.0660.0660.0660.0670.0660.0660.0660.2390.1300.1620.1650.0920.0780.0570.0550.0560.0560.0550.0560.0550.0550.0560.0540.0550.0540.0560.0570.0580.0560.0560.0550.0570.0570.0550.0560.0540.0560.0550.0590.0560.0550.0530.0590.0540.0540.0560.0540.0550.0540.0550.0560.0540.0560.0560.0550.0550.0550.0570.0610.0550.0550.0540.0540.0570.0560.0560.0590.0580.0580.0590.0550.0580.0560.0560.0550.0550.0550.0550.0550.0550.0570.0540.0570.0550.0550.0590.0560.0550.0550.0540.0570.0550.0570.0540.0540.0540.0540.0570.0550.0540.0550.0570.0590.0580.0590.0560.0550.0560.0560.0550.0560.0560.0560.0550.0560.0560.0550.0550.0590.0550.0530.0550.0570.0570.0560.0550.0530.0550.0540.0540.0550.0560.0550.0560.0550.0740.0380.0560.0480.0350.0720.0340.0490.0420.0530.0710.0240.0310.0370.0100.0570.0000.0731.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
state_top0.0310.3120.1350.0940.1390.1560.1380.2360.2020.2240.1040.4140.9870.1540.1530.1550.1530.1540.1550.1530.1540.1540.1550.1530.1540.1540.1530.1550.1530.1530.1530.1530.1550.1540.1530.1530.2370.2510.0720.2350.2160.0440.0320.0330.0380.0350.0310.0350.0300.0310.0300.0300.0300.0310.0340.0300.0350.0310.0320.0310.0300.0340.0310.0300.0300.0430.0380.0370.0340.0320.0300.0650.0300.0300.0300.0300.0300.0300.0300.0350.0330.0460.0520.0310.0330.0440.0390.0490.0320.0320.0310.0300.0410.0350.0350.0500.0450.0410.0370.0320.0410.0390.0380.0370.0310.0350.0400.0330.0360.0370.0310.0370.0430.0310.0360.0350.0330.0330.0300.0570.0300.0460.0310.0300.0300.0310.0350.0410.0300.0370.0380.0420.0310.0560.0360.0330.0380.0500.0370.0400.0430.0440.0370.0400.0360.0380.0300.0370.0330.0300.0310.0390.0560.0380.0390.0300.0320.0300.0300.0330.0400.0310.0320.0320.0360.0470.0390.0880.0210.0270.0100.0750.0490.0470.1750.0110.0140.0180.0060.0280.0070.0351.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
state_top_CA0.0320.3810.0520.0360.0540.0600.0530.0910.0780.0860.0400.1600.3810.1110.1110.1110.1110.1110.1110.1110.1100.1110.1100.1110.1100.1120.1100.1110.1100.1110.1100.1110.1120.1110.1100.1110.0220.0580.0220.0670.1100.0340.0320.0330.0330.0330.0330.0330.0320.0320.0320.0320.0320.0330.0330.0320.0340.0320.0320.0320.0320.0330.0330.0320.0320.0370.0330.0360.0330.0320.0320.0420.0320.0330.0320.0320.0320.0320.0320.0350.0330.0390.0400.0330.0330.0360.0360.0430.0340.0330.0330.0320.0370.0350.0330.0400.0390.0370.0370.0330.0370.0350.0330.0330.0330.0330.0340.0330.0330.0350.0320.0340.0350.0330.0360.0350.0340.0350.0320.0390.0320.0370.0320.0320.0330.0330.0330.0330.0320.0340.0370.0370.0330.0430.0370.0340.0340.0340.0330.0330.0340.0340.0330.0340.0320.0330.0320.0360.0340.0330.0330.0360.0410.0350.0370.0320.0320.0320.0320.0320.0340.0330.0320.0330.0100.0350.0010.0130.0080.0090.0040.0110.0050.0390.0520.0000.0100.0090.0000.0210.0030.0241.0001.0001.0000.0320.0420.0300.0370.0350.3860.0600.1190.0480.032
state_top_CO0.0000.1390.0190.0130.0200.0220.0190.0330.0280.0320.0150.0580.1390.0090.0090.0110.0090.0100.0100.0090.0090.0100.0130.0100.0090.0100.0090.0090.0100.0100.0090.0100.0120.0100.0090.0090.0190.0300.0070.0320.0100.0130.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0150.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0150.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0150.0140.0160.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0150.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0150.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0140.0040.0050.0040.0160.0030.0040.0030.0110.0140.0220.0260.0010.0050.0040.0020.0080.0010.0121.0001.0000.0321.0000.0150.0110.0130.0130.1410.0220.0430.0180.012
state_top_FL0.0050.1860.0250.0180.0260.0290.0260.0440.0380.0420.0190.0780.1860.0160.0160.0160.0170.0160.0170.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0180.0160.0160.0160.0250.0340.0040.0340.0160.0100.0110.0120.0130.0130.0110.0110.0100.0100.0100.0100.0110.0100.0110.0100.0120.0100.0100.0100.0100.0120.0100.0100.0110.0120.0120.0110.0100.0100.0100.0180.0110.0100.0100.0100.0100.0110.0100.0100.0110.0120.0130.0100.0110.0130.0120.0120.0110.0100.0100.0100.0130.0100.0120.0120.0120.0110.0110.0100.0110.0120.0120.0110.0120.0110.0120.0120.0120.0110.0120.0120.0130.0110.0110.0110.0120.0100.0100.0160.0100.0140.0100.0100.0100.0100.0120.0140.0100.0120.0110.0120.0100.0130.0110.0110.0120.0150.0160.0160.0150.0150.0140.0130.0120.0140.0100.0110.0110.0100.0110.0110.0150.0100.0120.0100.0100.0100.0100.0120.0130.0110.0100.0110.0120.0000.0040.0270.0030.0100.0000.0150.0150.0350.0310.0020.0020.0010.0020.0070.0020.0081.0001.0000.0420.0151.0000.0150.0180.0170.1880.0290.0580.0230.016
state_top_GA0.0130.1330.0180.0120.0190.0210.0180.0320.0270.0300.0140.0560.1330.0220.0220.0250.0220.0220.0230.0220.0230.0220.0250.0220.0220.0220.0220.0220.0220.0220.0220.0220.0220.0220.0220.0220.0310.0300.0010.0260.0220.0000.0030.0020.0030.0050.0010.0060.0010.0060.0000.0000.0010.0020.0060.0020.0050.0090.0010.0000.0000.0060.0000.0030.0000.0040.0050.0030.0070.0000.0000.0130.0010.0000.0010.0000.0010.0000.0000.0040.0030.0050.0090.0020.0050.0070.0040.0090.0020.0000.0030.0000.0070.0060.0070.0080.0070.0030.0020.0000.0070.0080.0080.0070.0000.0070.0060.0070.0080.0060.0030.0040.0090.0000.0030.0050.0050.0000.0000.0110.0000.0100.0020.0000.0000.0080.0050.0060.0000.0050.0030.0050.0000.0130.0040.0040.0030.0070.0060.0080.0080.0100.0060.0060.0080.0060.0000.0020.0010.0000.0000.0030.0120.0100.0060.0000.0060.0000.0030.0040.0070.0000.0020.0020.0030.0070.0000.0180.0020.0020.0000.0030.0060.0190.0240.0020.0020.0000.0000.0020.0010.0001.0001.0000.0300.0110.0151.0000.0130.0120.1340.0210.0410.0170.011
state_top_IL0.0150.1610.0220.0150.0230.0250.0230.0390.0330.0370.0170.0680.1610.0350.0350.0370.0350.0350.0350.0350.0360.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0670.0440.0200.0260.0340.0060.0070.0090.0110.0080.0070.0100.0070.0070.0070.0070.0070.0070.0080.0070.0080.0070.0070.0070.0070.0090.0070.0070.0070.0120.0100.0110.0100.0070.0070.0230.0070.0070.0070.0070.0070.0070.0070.0070.0070.0140.0170.0070.0080.0140.0130.0150.0130.0100.0080.0070.0120.0080.0100.0150.0120.0140.0110.0070.0120.0140.0130.0130.0070.0110.0140.0100.0110.0100.0090.0130.0140.0070.0120.0100.0070.0080.0080.0150.0070.0090.0080.0070.0070.0070.0140.0130.0070.0110.0080.0150.0070.0190.0110.0080.0130.0190.0110.0120.0150.0150.0100.0130.0110.0110.0070.0110.0070.0070.0070.0140.0200.0100.0120.0070.0070.0070.0070.0080.0120.0110.0070.0080.0070.0020.0120.0220.0020.0050.0050.0130.0050.0270.0340.0030.0010.0070.0000.0050.0000.0081.0001.0000.0370.0130.0180.0131.0000.0150.1640.0250.0500.0200.014
state_top_MA0.0000.1540.0210.0150.0220.0240.0220.0370.0310.0350.0160.0650.1540.0500.0480.0480.0480.0490.0480.0490.0480.0490.0490.0480.0510.0480.0480.0480.0480.0480.0480.0480.0490.0480.0480.0480.0280.0330.0010.0310.0480.0000.0040.0040.0070.0050.0030.0060.0000.0030.0000.0000.0010.0000.0030.0000.0040.0000.0010.0050.0000.0010.0040.0000.0000.0030.0070.0100.0010.0000.0000.0190.0010.0020.0000.0000.0030.0000.0000.0000.0030.0130.0150.0020.0050.0090.0090.0110.0000.0000.0000.0000.0080.0050.0080.0140.0120.0110.0100.0020.0100.0000.0050.0070.0000.0040.0070.0040.0050.0090.0030.0070.0090.0020.0040.0050.0000.0040.0000.0200.0060.0210.0100.0000.0000.0000.0000.0070.0030.0050.0000.0110.0050.0190.0060.0050.0070.0090.0000.0030.0080.0060.0060.0070.0030.0050.0000.0100.0060.0000.0000.0080.0150.0050.0040.0020.0010.0000.0010.0040.0060.0000.0000.0020.0000.0150.0090.0150.0050.0100.0020.0150.0000.0280.0320.0000.0000.0000.0030.0020.0000.0031.0001.0000.0350.0130.0170.0120.0151.0000.1560.0240.0480.0190.013
state_top_Missing0.0480.9870.1350.0940.1390.1560.1390.2360.2020.2240.1040.4140.9870.2110.2100.2130.2100.2110.2110.2100.2100.2110.2110.2100.2100.2100.2100.2110.2100.2100.2100.2100.2130.2110.2100.2100.2110.2460.0040.2250.2100.0250.0280.0300.0400.0340.0260.0340.0240.0240.0230.0230.0230.0230.0320.0230.0340.0240.0260.0260.0230.0310.0260.0230.0230.0480.0400.0360.0300.0260.0230.0840.0230.0230.0230.0230.0230.0230.0230.0320.0300.0530.0630.0260.0290.0510.0410.0560.0260.0250.0240.0230.0460.0310.0320.0590.0520.0430.0360.0270.0440.0410.0390.0360.0250.0320.0430.0290.0350.0380.0240.0380.0490.0240.0320.0330.0280.0290.0230.0710.0230.0530.0230.0230.0240.0240.0320.0440.0230.0380.0360.0460.0240.0690.0350.0290.0380.0590.0370.0420.0480.0500.0370.0430.0350.0390.0230.0380.0290.0230.0250.0400.0700.0380.0420.0230.0280.0230.0230.0290.0440.0250.0270.0270.0240.0380.0270.0840.0150.0100.0020.0720.0450.1140.1740.0060.0030.0030.0040.0100.0080.0071.0001.0000.3860.1410.1880.1340.1640.1561.0000.2670.5260.2140.144
state_top_NY0.0170.2640.0360.0250.0370.0420.0370.0630.0540.0600.0280.1110.2640.0740.0740.0740.0740.0740.0740.0740.0730.0740.0740.0730.0740.0740.0740.0740.0740.0740.0740.0730.0740.0740.0740.0740.0200.0940.0530.1220.0730.0140.0150.0160.0140.0160.0140.0160.0150.0140.0130.0130.0130.0150.0150.0140.0160.0140.0140.0140.0130.0150.0130.0150.0130.0150.0150.0170.0160.0150.0130.0270.0130.0130.0140.0140.0150.0140.0130.0180.0150.0220.0240.0140.0190.0200.0170.0250.0140.0160.0140.0130.0200.0190.0160.0260.0200.0180.0170.0180.0210.0180.0160.0160.0140.0150.0150.0140.0150.0190.0130.0190.0180.0140.0200.0180.0150.0150.0130.0220.0140.0200.0130.0140.0130.0130.0180.0160.0140.0170.0210.0190.0140.0260.0160.0150.0190.0150.0150.0170.0170.0180.0160.0170.0170.0170.0130.0170.0160.0130.0140.0170.0280.0190.0170.0130.0170.0130.0140.0150.0180.0140.0160.0140.0220.0030.0290.0130.0130.0100.0020.0140.0200.0450.0420.0000.0000.0030.0020.0100.0000.0091.0001.0000.0600.0220.0290.0210.0250.0240.2671.0000.0820.0330.022
state_top_Other0.0350.5190.0710.0490.0730.0820.0730.1240.1060.1180.0550.2180.5190.0910.0910.0930.0910.0910.0940.0910.0910.0910.0910.0910.0910.0910.0910.0950.0910.0910.0910.0910.0920.0910.0910.0910.1970.1510.0420.1070.0910.0090.0130.0140.0230.0170.0110.0170.0100.0110.0100.0100.0090.0090.0160.0090.0150.0120.0140.0120.0100.0150.0110.0090.0100.0230.0230.0120.0120.0120.0090.0420.0100.0100.0110.0090.0090.0100.0090.0120.0130.0210.0270.0110.0110.0240.0160.0180.0090.0090.0090.0100.0170.0100.0120.0210.0210.0170.0110.0110.0160.0160.0180.0140.0100.0130.0200.0120.0160.0130.0100.0150.0250.0090.0090.0110.0100.0100.0090.0350.0100.0210.0090.0090.0090.0090.0140.0260.0090.0200.0110.0180.0090.0260.0110.0100.0150.0370.0170.0200.0230.0250.0180.0200.0190.0190.0100.0130.0100.0090.0090.0170.0290.0120.0170.0090.0120.0090.0090.0130.0200.0110.0140.0110.0190.0050.0090.0520.0090.0170.0070.0520.0250.0730.1030.0050.0070.0130.0030.0120.0040.0171.0001.0000.1190.0430.0580.0410.0500.0480.5260.0821.0000.0660.044
state_top_TX0.0220.2110.0290.0200.0300.0330.0300.0500.0430.0480.0220.0880.2110.0400.0400.0410.0400.0400.0410.0400.0410.0400.0410.0400.0400.0400.0400.0410.0400.0400.0400.0400.0410.0400.0400.0400.0400.0530.0080.0530.0400.0010.0000.0000.0090.0050.0010.0080.0000.0010.0000.0030.0000.0000.0040.0000.0030.0000.0070.0050.0030.0050.0040.0000.0000.0170.0090.0060.0060.0050.0040.0180.0000.0000.0000.0000.0000.0000.0000.0040.0090.0110.0130.0010.0010.0090.0090.0110.0000.0020.0050.0000.0080.0000.0020.0140.0090.0100.0080.0000.0070.0080.0090.0100.0010.0090.0110.0030.0060.0070.0030.0050.0080.0000.0000.0030.0060.0050.0000.0160.0000.0080.0020.0000.0000.0000.0010.0060.0000.0060.0030.0080.0000.0150.0030.0000.0040.0150.0070.0100.0110.0130.0100.0100.0080.0090.0000.0080.0020.0000.0000.0050.0150.0100.0070.0000.0040.0000.0020.0050.0090.0000.0000.0060.0030.0110.0070.0220.0020.0080.0020.0130.0130.0280.0370.0070.0000.0020.0000.0010.0000.0031.0001.0000.0480.0180.0230.0170.0200.0190.2140.0330.0661.0000.018
state_top_WA0.0300.1420.0190.0130.0200.0220.0200.0340.0290.0320.0150.0600.1420.0350.0350.0360.0350.0350.0350.0350.0350.0350.0390.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0330.0330.0000.0290.0350.0080.0080.0090.0080.0070.0080.0070.0070.0080.0070.0070.0070.0070.0070.0070.0080.0070.0070.0070.0080.0080.0070.0080.0070.0110.0080.0080.0110.0080.0070.0110.0080.0080.0080.0090.0070.0070.0070.0080.0070.0110.0110.0090.0090.0100.0080.0120.0100.0070.0070.0070.0100.0080.0080.0120.0100.0090.0080.0080.0090.0100.0090.0090.0080.0100.0120.0070.0100.0100.0070.0070.0080.0070.0090.0080.0070.0080.0080.0110.0080.0110.0070.0070.0080.0090.0070.0080.0070.0080.0170.0090.0070.0130.0090.0100.0130.0100.0110.0110.0110.0090.0080.0100.0080.0090.0070.0080.0090.0070.0090.0080.0100.0100.0070.0070.0090.0070.0070.0080.0090.0070.0090.0080.0000.0090.0050.0060.0000.0000.0000.0120.0090.0170.0230.0060.0040.0050.0000.0040.0020.0061.0001.0000.0320.0120.0160.0110.0140.0130.1440.0220.0440.0181.000

Missing values

2023-12-31T13:47:29.946686image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-31T13:47:38.604752image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-31T13:48:01.504902image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

account_idis_current_customeris_self_serviceis_arr_over_12kcompany_revenue_bucketcountrystateindustry_groupedhas_crossbeam_datacrossbeam_product1_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customercrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customerdnb_founded_time_groupedhas_hg_datahg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99hg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148country_topstate_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_USstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WAindustry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_nanrevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanfounded_After 2000founded_Before 2000founded_nan
0001PY000002SuJDYA0False0.00.0Under 10BCHNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Before 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
10011G00000nEkzYQASFalse0.00.0NaNNLNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NLMissingFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
20011G00000qFM5EQAWFalse0.00.0NaNNaNNaNNaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0MissingMissingFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
30011G00000tKtSbQAKFalse0.00.0NaNRONaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
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